Skip to main content Accessibility help
×
Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-29T16:31:01.797Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  05 March 2016

Ryen W. White
Affiliation:
Microsoft Research
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aalbersberg, I.J. (1992). Incremental relevance feedback. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 11–22). ACM Press.
Abowd, G.D., Atkeson, C.G., Hong, J., Long, S., Kooper, R., and Pinkerton, M. (1997). Cyberguide: A mobile context-aware tour guide. ACM Wireless Networks, 3, 421–433.Google Scholar
Abowd, G.D. and Mynatt, E.D. (2000). Charting past, present, and future research in ubiquitous computing. ACM Transactions on Computer-Human Interaction, 7(1), 29–58.Google Scholar
Abrams, D., Baecker, R., and Chignell, M. (1998). Information archiving with bookmarks: Personal Web space construction and organization. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 41–48). ACM Press/Addison-Wesley Publishing Co.
Ackerman, M.A. and McDonald, D.W. (1998). Just talk to me: A field study of expertise location. In Proceedings of conference on computer supported cooperative work (pp. 315–324). ACM Press.
Ackerman, M.S. and McDonald, D.W. (1996). Answer Garden 2: Merging organizational memory with collaborative help. In Proceedings of conference on computer supported cooperative work (pp. 97–105). ACM Press.
Adamczyk, P.D. and Bailey, B.P. (2004). If not now, when? The effects of interruption at different moments within task execution. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 271–278). ACM Press/Addison-Wesley Publishing Co.
Adamic, L.A., Zhang, J., Bakshy, E., and Ackerman, M.S. (2008). Knowledge sharing and yahoo answers: Everyone knows something. In Proceedings of the international conference on the World Wide Web (pp. 665–674). ACM Press.
Adar, E. (2007). User 4xxxxx9: anonymizing query logs. In Proceedings of the query log analysis workshop at the international conference on the World Wide Web.
Adar, E., Teevan, J., and Dumais, S.T. (2008). Large scale analysis of web revisitation patterns. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1197–1206). ACM Press/Addison-Wesley Publishing Co.
Adar, E., Teevan, J., and Dumais, S.T. (2009). Resonance on the web: Web dynamics and revisitation patterns. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1381–1390). ACM Press/Addison-Wesley Publishing Co.
Agapie, E., Golovchinsky, G., and Qvarfordt, P. (2013). Leading people to longer queries. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 3019–3022). ACM Press/Addison-Wesley Publishing Co.
Agarawala, A. and Balakrishnan, R. (2006). Keepin'it real: Pushing the desktop metaphor with physics, piles and the pen. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1283–1292). ACM Press/Addison-Wesley Publishing Co.
Agarwal, A., Chakrabarti, S., and Aggarwal, S. (2006). Learning to rank networked entities. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 14–23). ACM Press.
Agarwal, D., Li, L., and Smola, A.J. (2011). Linear-time estimators for propensity scores. In Proceedings of the international conference on artificial intelligence and statistics (pp. 93–100).
Ageev, M., Guo, Q., Lagun, D., and Agichtein, E. (2011). Find it if you can: A game for modeling different types of web search success using interaction data. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 345–354). ACM Press.
Agichtein, E., Brill, E., Dumais, S., and Ragno, R. (2006). Learning user interaction models for predicting web search result preferences. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 3–10). ACM Press.
Agichtein, E., White, R.W., Dumais, S.T., and Bennett, P.N. (2012). Search, interrupted: understanding and predicting search task continuation. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 315–324). ACM Press.
Agichtein, E. and Zheng, Z. (2006). Identifying best bet web search results by mining past user behavior. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 902–908). ACM Press.
Agosti, M., Fuhr, N., Toms, E., and Vakkari, P. (2014). Evaluation methodologies in information retrieval (Dagstuhl seminar 13441), Dagstuhl Reports, 3(10), 92–126.Google Scholar
Agrawal, R., Gollapudi, S., Halverson, A., and Ieong, S. (2009). Diversifying search results. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 5–14). ACM Press.
Ahlberg, C. and Shneiderman, B. (1994). Visual information seeking: Tight coupling of dynamic query filters with starfield displays. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 313–317). ACM Press/Addison-Wesley Publishing Co.
Ahlberg, C., Williamson, C., and Shneiderman, B. (1992). Dynamic queries for information exploration: An implementation and evaluation. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 619–626). ACM Press/Addison-Wesley Publishing Co.
Ahmad, F. and Kondrak, G. (2005). Learning a spelling error model from search query logs. In Proceedings of the conference on human language technology and empirical methods in natural language processing (pp. 955–962). Association for Computational Linguistics.
Ahn, J.W. and Brusilovsky, P. (2013). Adaptive visualization for exploratory information retrieval. Information Processing and Management, 49(5), 1139–1164.Google Scholar
Ahn, J.W., Brusilovsky, P., Grady, J., He, D., and Syn, S.Y. (2007). Open user profiles for adaptive news systems: Help or harm?. In Proceedings of the international conference on the World Wide Web (pp. 11–20). ACM Press.
Ali, K. and Chang, C. (2006). On the relationship between click-rate and relevance for search engines. In Proceedings of data-mining and information engineering conference (pp. 213–222). ACM Press.
Allan, J. (1995). Relevance feedback with too much data. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 337–343). ACM Press.
Allan, J. (1996). Incremental relevance feedback for information filtering. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 270–278). ACM Press.
Allan, J. (2005). HARD track overview in TREC 2004: High accuracy retrieval from documents. In Proceedings of TREC 2004 (pp. 25–35). NIST special publication 500-261.
Allan, J., Carterette, B., and Lewis, J. (2005). When will information retrieval be “good enough”? In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 433–440). ACM Press.
Allan, J., Croft, B., Moffat, A., and Sanderson, M. (2012). Frontiers, challenges, and opportunities for information retrieval: report from SWIRL 2012 the second strategic workshop on information retrieval in Lorne. SIGIR Forum, 46(1), 2–32.Google Scholar
Allen, J. (1987). Natural language understanding. New York: Benjamin/Cummings Publishing Company.
Allen, J.F., Byron, D.K., Dzikovska, M., Ferguson, G., Galescu, L., and Stent, A. (2001). Toward conversational human-computer interaction. AI Magazine, 22(4), 27.Google Scholar
Al-Maskari, A., Sanderson, M., Clough, P., and Airio, E. (2008). The good and the bad system: Does the test collection predict users’ effectiveness? In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 59–66). ACM Press.
Almeida, R.B. and Almeida, V.A. (2004). A community-aware search engine. In Proceedings of the international conference on the World Wide Web (pp. 413–421). ACM Press.
Alonso, O., Rose, D.E., and Stewart, B. (2008). Crowdsourcing for relevance evaluation. SIGIR Forum, 42(2), 9–15. ACM Press.
Amar, R., Eagan, J., and Stasko, J. (2005). Low-level components of analytic activity in information visualization. In Proceedings of the IEEE symposium on information visualization (pp. 111–117). IEEE.
Amari, S.I., Cichocki, A., and Yang, H.H. (1996). A new learning algorithm for blind signal separation. In Proceedings of the conference on neural information processing systems (pp. 757–763). ACM Press.
Amento, B., Terveen, L., and Hill, W. (2000). Does “authority” mean quality? Predicting expert quality ratings of Web documents. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 296–303). ACM Press.
Amershi, S. and Morris, M.R. (2008). CoSearch: A system for co-located collaborative web search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1647–1656). ACM Press/Addison-Wesley Publishing Co.
Amitay, E., Har'El, N., Sivan, R., and Soffer, A. (2004). Web-a-where: geotagging web content. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 273–280). ACM Press.
Anand, K., Mathew, G., and Reddy, V. (1995). Blind separation of multiple co-channel BPSK signals arriving at an antenna array. IEEE Signal Processing Letters, 2, 176–178.Google Scholar
Anderson, A., Huttenlocher, D., Kleinberg, J., and Leskovec, J. (2013). Steering user behavior with badges. In Proceedings of the nternational conference on the World Wide Web (pp. 95–106). International World Wide Web Conferences Steering Committee.
Anderson, A., Huttenlocher, D., Kleinberg, J., and Leskovec, J. (2014). Engaging with massive online courses. In Proceedings of the international conference on World Wide Web (pp. 687–698). International World Wide Web Conferences Steering Committee.
Anderson, J.R. (1976). Language, Memory, and Thought. Mahwah, NJ: Earlbaum.
Anderson, J.R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22(3), 261–295.Google Scholar
Anderson, J.R. (1990). The Adaptive Character of Thought. New York: Psychology Press.
Anderson, J.R. and Lebiere, C. (1998). The Atomic Components of Thought. Mahwah, NJ: Lawrence Erlbaum Associates.
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., and Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 1036–1060.
Anderson, L., Krathwohl, D., Airasian, P., Cruikshank, K., Mayer, R., Pintrich, P., and Wittrock, M. (2000). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives, Abridged Version. Boston: Allyn and Bacon.
Anderson, L.W., Krathwohl, D.R., and Bloom, B.S. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives. Boston: Allyn & Bacon.
Anderson, M.L. (2003). Embodied cognition: A field guide. Artificial Intelligence, 149(1), 91–130.Google Scholar
Andrade, L. and Silva, M. (2006). Relevance ranking for geographic IR. In Proceedings of the ACM SIGIR workshop on geographic information retrieval.
André, P., Teevan, J. and Dumais, S. (2009a). From x-rays to silly putty via Uranus: Serendipity and its role in Web search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2033–2036). ACM Press/Addison-Wesley Publishing Co.
André, P., Teevan, J., and Dumais, S.T. (2009b). Discovery is never by chance: Designing for (un)serendipity. In Proceedings of the ACM conference on creativity and cognition (pp. 305–314). ACM Press.
Anick, P. (2003). Using terminological feedback for web search refinement: A log based study. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 88–95). ACM Press.
Anick, P. and Tipirneni, S. (1999). The paraphrase search assistant: Terminological feedback for iterative information seeking. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 153–159). ACM Press.
Anick, P.G., Brennan, J.D., Flynn, R.A., Hanssen, D.R., Alvey, B., and Robbins, J.M. (1989). A direct manipulation interface for boolean information retrieval via natural language query. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 135–150). ACM Press.
Antin, J. and Churchill, E.F. (2011). Badges in social media: A social psychological perspective. In Proceedings of the gamification workshop at the ACM SIGCHI conference (pp. 1–4). ACM Press.
Antin, J., de Sa, M., and Churchill, E.F. (2012). Local experts and online review sites. In Proceedings of the ACM CSCW conference on computer supported cooperative work companion (pp. 55–58). ACM Press.
Arapakis, I., Bai, X., and Cambazoglu, B.B. (2014). Impact of response latency on user behavior in web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 103–112). ACM Press.
Arapakis, I., Jose, J.M., and Gray, P.D. (2008). Affective feedback. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 20–24). ACM Press.
Arapakis, I., Konstas, I., and Jose, J.M. (2009). Using facial expressions and peripheral physiological signals as implicit indicators of topical relevance. In Proceedings of the ACM international conference on multimedia (pp. 461–470). ACM Press.
Arlitt, M. (2000). Characterizing Web user sessions. ACM SIGMETRICS Performance Evaluation Review, 28(2), 50–63.Google Scholar
Armstrong, R., Freitag, D., Joachims, T., and Mitchell, T. (1995). WebWatcher: A learning apprentice for the world wide web. In Proceedings of the AAAI spring symposium on information gathering from heterogeneous, distributed environments (pp. 6–12). AAAI.
Arrington, M. (2006). AOL proudly releases massive amounts of private data. http://techcrunch.com/2006/08/06/aol-proudly-releases-massive-amounts-of-user-search-data/.
Article 29 Data Protection Working Party. (2008). Opinion on data protection issues related to search engines. http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommendation/files/2008/wp148_en.pdf. Accessed on October 12, 2015.
Attfield, S., Blandford, A., and Dowell, J. (2003). Information seeking in the context of writing: A design psychology interpretation of the “problematic situation.” Journal of Documentation, 59(4), 430–453.Google Scholar
Auer, P. (2003). Using confidence bounds for exploitation-exploration trade-offs. The Journal of Machine Learning Research, 3, 397–422.Google Scholar
Aula, A. and Nordhausen, K. (2006). Modeling successful performance in web searching. Journal of the Asosication for Information Science and Technology, 57(12), 1678–1693.Google Scholar
Aula, A., Jhaveri, N., and Käki, M. (2005a). Information search and re-access strategies of experienced web users. In Proceedings of the international conference on the World Wide Web (pp. 583–592). International World Wide Web Conferences Steering Committee.
Aula, A., Khan, R.M., and Guan, Z. (2010a). How does search behavior change as search becomes more difficult? In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 35–44). ACM Press/Addison-Wesley Publishing Co.
Aula, A., Khan, R.M., Guan, Z., Fontes, P., and Hong, P. (2010b). A comparison of visual and textual page previews in judging the helpfulness of web pages. In Proceedings of the international conference on the World Wide Web (pp. 51–60). ACM Press.
Aula, A., Majaranta, P., and Räihä, K.J. (2005b). Eye-tracking reveals the personal styles for search result evaluation. In Proceedings of human-computer interaction-INTERACT 2005 (pp. 1058–1061). Springer Berlin Heidelberg.
Aula, A. and Siirtola, H. (2005). Hundreds of folders or one ugly pile–strategies for information search and re-access. In Proceedings of Human-Computer Interaction-INTERACT 2005 (pp. 954–957). Springer Berlin Heidelberg.
Avrahami, A., Fussel, S., and Hudson, S. (2008). IM waiting: Timing and responsiveness in semi-synchronous communication. In Proceedings of the ACM conference on computer supported cooperative work (pp. 285–294). ACM Press.
Ayers, E. and Stasko, J. (1995). Using graphic history in browsing the World Wide Web. In Proceedings of the international conference on the World Wide Web. International World Wide Web Conferences Steering Committee.
Azcarraga, J. and Suarez, M.T. (2012). Predicting academic emotions based on brainwaves, mouse behaviour and personality profile. In Proceedings of PRICAI: Trends in artificial intelligence (pp. 728–733). Springer Berlin Heidelberg.
Azizyan, M., Constandache, I., and Choudhury, R.R. (2009). Surroundsense: Mobile phone localization via ambience fingerprinting. In Proceedings of the annual internation conference on mobile computing and networking (pp. 261–272). ACM Press.
Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., and MacIntyre, B. (2001). Recent advances in augmented reality. IEEE Computer Graphics and Applications, 21(6), 34–47.Google Scholar
Azzopardi, L. (2011). The economics in interactive information retrieval. In Proceedings of the ACM SIGIR conference on research and development in Information Retrieval (pp. 15–24). ACM Press.
Azzopardi, L. (2014). Modelling interaction with economic models of search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 3–12). ACM Press.
Azzopardi, L., Järvelin, K., Kamps, J., and Smucker, M.D. (2011). Report on the SIGIR 2010 workshop on the simulation of interaction. ACM SIGIR Forum, 44(2), 35–47. ACM Press.
Azzopardi, L., Kelly, D., and Brennan, K. (2013). How query cost affects search behavior. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 23–32). ACM Press.
Back, J. and Oppenheim, C. (2001). A model of cognitive load for IR: Implications for user relevance feedback interaction. Information Research, 6(2).Google Scholar
Baeza-Yates, R. and Ribeiro-Neto, B. (1999). Modern Information Retrieval. Boston, MA: Addison-Wesley Longman Publishing Company.
Bharat, K. (2000). SearchPad: Explicit capture of search context to support Web search. In Proceedings of the international World Wide Web conference on computer networks (pp. 493–501).
Bai, X., Cambazoglu, B.B., and Junqueira, F.P. (2011). Discovering URLs through user feedback. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 77–86). ACM Press.
Bailey, J.E. and Pearson, S.W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545.Google Scholar
Bailey, P., White, R.W., Liu, H., and Kumaran, G. (2010). Mining historic query trails to label long and rare search engine queries. ACM Transactions on the Web, 4(4), 15.Google Scholar
Balabanovic, M. and Shoham, Y. (1995). Learning information retrieval agents: Experiments with automated web browsing. In Proceedings of the AAAI spring symposium on information gathering from heterogeneous, distributed environments (pp. 13–18).
Balabanović, M. and Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3), 66–72.Google Scholar
Balog, K., Azzopardi, L., and De Rijke, M. (2006). Formal models for expert finding in enterprise corpora. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 43–50). ACM Press.
Balog, K. and de Rijke, M. (2007). Determining expert profiles (with an application to expert finding). In Proceedings of the international joint conference on artificial intelligence (pp. 2657–2662). Morgan Kaufmann Publishers.
Bandura, A. (1971). Psychological Modelling. New York: Lieber-Antherton.
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.
Banko, M. and Brill, E. (2001). Scaling to very very large corpora for natural language disambiguation. In Proceedings of the annual meeting of the association for computational linguistics (pp. 26–33). Association for Computational Linguistics.
Banko, M., Brill, E., Dumais, S., and Lin, J. (2002). AskMSR: Question answering using the worldwide Web. In Proceedings of 2002 AAAI spring symposium on mining answers from texts and knowledge bases (pp. 7–9). AAAI Press.
Barbaro, M., Zeller, T., and Hansell, S. (2006). A face is exposed for AOL searcher no. 4417749. New York Times, 9(2008), F8.Google Scholar
Barber, R.K. and Merton, E. (2006). The travels and adventures of serendipity: A study in sociological semantics and the sociology of science (paperback ed.). Princeton, NJ: Princeton University Press.
Bar-Ilan, J. (2007). Position paper: Access to query logs – an academic researcher's point of view. In Proceedings of the query log analysis workshop at the international conference on the World Wide Web.
Barnett, M., Chandramouli, B., DeLine, R., Drucker, S., Fisher, D., Goldstein, J., Morrison, P., and Platt, J. (2013). Stat! An interactive analytics environment for big data. In Proceedings of the ACM SIGMOD international conference on management of data (pp. 1013–1016). ACM Press.
Baron-Cohen, S., Cox, A., Baird, G., Swettenham, J., Nightingale, N., Morgan, K., and Charman, T. (1996). Psychological markers in the detection of autism in infancy in a large population. The British Journal of Psychiatry, 168(2), 158–163.Google Scholar
Barroso, L.A., Dean, J., and Holzle, U. (2003). Web search for a planet: The Google cluster architecture. Micro, 23(2), 22–28. IEEE.Google Scholar
Barry, C.L. (1994). User-defined relevance criteria: An exploratory study. Journal of the American Society for Information Science, 45(3), 149–159.Google Scholar
Barry, C.L. (1998). Document representations and clues to document relevance. Journal of the American Society for Information Science, 49(14), 1293–1303.Google Scholar
Bar-Yossef, Z. and Gurevich, M. (2008). Mining search engine query logs via suggestion sampling. In Proceedings of the VLDB Endowment (pp. 54–65).Google Scholar
Bar-Yossef, Z. and Kraus, N. (2011). Context-sensitive query auto-completion. In Proceedings of the international conference on the World Wide Web (pp. 107–116). ACM Press.
Baskaya, F.Keskustalo, H., and Järvelin, K. (2013). Modeling behavioral factors in interactive information retrieval. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 2297–2302). ACM Press.
Baskaya, F., Keskustalo, H., and Järvelin, K. (2012). Time drives interaction: simulating sessions in diverse searching environments. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 105–114). ACM Press.
Basu, C., Hirsh, H., and Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the AAAI conference on artificial intelligence (pp. 714–720). AAAI Press.
Bateman, S., Teevan, J., and White, R.W. (2012). The search dashboard: how reflection and comparison impact search behavior. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1785–1794). ACM Press/Addison-Wesley Publishing Co.
Bates, M. (1989). The design of browsing and berry-picking techniques for the online search interface. Online Review, 13(5), 407–424.Google Scholar
Bates, M. (1995). Models of natural language understanding. In Proceedings of the National Academy of Sciences, 92(22), 9977–9982.Google Scholar
Bates, M.J. (1979a). Information search tactics. Journal of the American Society for information Science, 30(4), 205–214.Google Scholar
Bates, M.J. (1979b). Idea tactics. Journal of the American Society for Information Science, 30(5), 280–289.Google Scholar
Bates, M.J. (1990). Where should the person stop and the information search interface start?Information Processing and Management, 26(5), 575–591.Google Scholar
Bates, M.J. (1998). Indexing and access for digital libraries and the Internet: Human, database, and domain factors. Journal of the American Society for Information Science, 49(13), 1185–1205.Google Scholar
Bates, M.J. (2002). The cascade of interactions in the digital library interface. Information Processing and Management, 38(3), 381–400.Google Scholar
Bates, M.J. (2005). Are there optimal ways to do exploratory searching? Slides available online at: http://research.microsoft.com/en-us/um/people/ryenw/xsi/slides/bates.pdf. Accessed on August 17, 2015.
Bates, M.J. (2007). What is browsing – really? A model drawing from behavioural science research. Information Research, 12(4), paper 330.Google Scholar
Battelle, J. (2005). The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture. New York: Penguin.
Bawden, D. (1986). Information systems and the stimulation of creativity. Journal of Information Science, 12(5), 203–216.Google Scholar
Baudisch, P. and Brueckner, L. (2002). TV Scout: Lowering the entry barrier to personalized TV program recommendation. In Proceedings of the international conference on adaptive hypermedia and adaptive web-based systems (pp. 58–68).
Baudisch, P., Tan, D., Collomb, M., Robbins, D., Hinckley, K., Agrawala, M., and Ramos, G. (2006). Phosphor: Explaining transitions in the user interface using afterglow effects. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 169–178). ACM Press.
Bawden, D. (1986). Information systems and the stimulation of creativity. Journal of Information Science, 12, 203–216.Google Scholar
Beale, R. (2007). Supporting serendipity: using ambient intelligence to augment user exploration for data mining and web browsing. International Journal of Human-Computer Studies, 65(5), 421–433.Google Scholar
Beaulieu, M. (1997). Experiments on interfaces to support query expansion. Journal of Documentation, 53(1), 8–19.Google Scholar
Beaulieu, M. and Jones, S. (1998). Interactive searching and interface issues in the Okapi best match retrieval system. Interacting with Computers, 10(3), 237–248.Google Scholar
Bederson, B. and Hollan, J. (1994). Pad++: A zooming graphical interface for exploring alternative interface physics. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 17–26). ACM Press.
Beeferman, D. and Berger, A.L. (2000). Agglomerative clustering of a search engine query log. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 407–416).
Belkin, N. (1996). Intelligent information retrieval: Whose intelligence? In Krause, J., Herfurth, M., Marx, J. (eds.). ISI ’96: Hearausfordurungen an die Informationswirtschaft Informationsverdichtung, Informationsbewertung and Datenvisualisierung. Konstanz, Germany, University of Konstanz. 25–31.
Belkin, N.J. (1978). Information concepts for information science. Journal of Documentation, 34(1), 55–85.Google Scholar
Belkin, N.J. (1980). Anomalous state of knowledge for information retrieval. Canadian Journal of Information Science, 5, 133–143.Google Scholar
Belkin, N.J. (1990). The cognitive viewpoint in information science. Journal of Information Science, 16(1), 11–15.Google Scholar
Belkin, N.J. (1993). Interaction with texts: Information retrieval as information-seeking behavior. In Proceedings of Information Retrieval (pp. 55–66).
Belkin, N.J. (2000). Helping people find what they don't know. Communications of the ACM, 43(8), 59–61.Google Scholar
Belkin, N.J. (2008). Some(what) grand challenges for information retrieval. ACM SIGIR Forum, 42(1), 47–54. ACM Press.
Belkin, N.J. and Cool, C.A. (2002). Classification of interactions with information. In Emerging frameworks and methods. In Proceedings of the COLIS conference on conceptions of library and information science (pp. 1–15). Libraries Unlimited.
Belkin, N.J., Cool, C., Croft, W.B., and Callan, J.P. (1993). The effect multiple query representations on information retrieval system performance. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 339–346). ACM Press.
Belkin, N.J., Cool, C., Kelly, D., Lee, H.-J., Muresan, G., Tang, M.C., and Yuan, X.J. (2003). Query length in interactive information retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 205–212). ACM Press.
Belkin, N.J., Cool, C., Kelly, D., Lin, S.-J., Park, S.-Y., Perez-Carballo, J., and Sikora, C. (2001). Iterative exploration, design and evaluation for query reformulation in interactive information retrieval. Information Processing and Management, 37(3), 403–434.Google Scholar
Belkin, N.J., Cool, C., and Koenemann, J. (1996). On the potential utility of negative relevance feedback for interactive information retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (p. 341). ACM Press.
Belkin, N.J., Cool, C., Stein, A., and Theil, U. (1995). Cases, scripts and information seeking strategies: On the design of interactive information retrieval systems. Expert Systems with Applications, 29(3), 325–344.Google Scholar
Belkin, N.J. and Croft, W.B. (1992). Information filtering and retrieval: Two sides of the same coin?Communications of the ACM, 35(12), 29–38.Google Scholar
Belkin, N.J., Oddy, R.N., and Brooks, H.M. (1982). ASK for information retrieval: Part I -background and theory. Journal of Documentation, 38(2), 61–71.Google Scholar
Belkin, N.J., Perez Carballo, J., Cool, C., Lin, S., Park, S.Y., Rieh, S.Y., Savage, P., Sikora, C., Xie, H., Cool, C., and Allan, J. (1998). Rutgers TREC-6 interactive track experience. In Proceedings of the text retrieval conference (pp. 597–610).
Bell, R.M. and Koren, Y. (2007). Lessons from the Netflix prize challenge. ACM SIGKDD Explorations Newsletter, 9(2), 75–79.Google Scholar
Bell, W.J. (1991). Searching Behaviour: The Behavioural Ecology of Finding Resources. London: Chapman and Hall.
Bendersky, M. and Croft, W.B. (2009). Analysis of long queries in a large scale search log. In Proceedings of the 2009 workshop on web search click data (pp. 8–14). ACM Press.
Benko, H., Wilson, A.D., and Baudisch, P. (2006). Precise selection techniques for multi-touch screens. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1263–1272). ACM Press.
Benford, S., Snowdon, D., Greenhalgh, C., Ingram, R., Knox, I., and Brown, C. (1995). VR-VIBE: A virtual environment for co-operative information retrieval. Computer Graphics Forum, 14(3), 349–360. Blackwell Science Ltd.
Bennett, P.N., Svore, K., and Dumais, S.T. (2010). Classification-enhanced ranking. In Proceedings of the international conference on the World Wide Web (pp. 111–120). ACM Press.
Bennett, P.N., Radlinski, F., White, R.W., and Yilmaz, E. (2011). Inferring and using location metadata to personalize web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 135–144). ACM Press.
Bennett, P.N., White, R.W., Chu, W., Dumais, S.T., Bailey, P., Borisyuk, F., and Cui, X. (2012). Modeling the impact of short-and long-term behavior on search personalization. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 185–194). ACM Press.
Berlyne, D.E. (1960). Conflict, arousal and curiosity, New York: McGraw Hill.
Bernstein, M.S., Tan, D., Smith, G., Czerwinski, M., and Horvitz, E. (2010). Personalization via friendsourcing. ACM Transactions on Computer-Human Interaction, 17(2), 6.Google Scholar
Bernstein, M.S., Teevan, J., Dumais, S., Liebling, D., and Horvitz, E. (2012). Direct answers for search queries in the long tail. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 237–246). ACM Press.
Bernstein, M., Van Kleek, M., Karger, D., and Schraefel, M.C. (2008). Information scraps: How and why information eludes our personal information management tools. ACM Transactions on Information Systems, 26(4), 24.Google Scholar
Bernstein, P. and Clarke-Stewart, , , R. (2008). Psychology, 8th edition. Boston, MA: Houghton Mifflin Company.
Bharat, K. (2000). SearchPad: Explicit capture of search context to support Web search. Computer Networks, 33(1), 493–501.Google Scholar
Bhavnani, S.K. (2002). Domain-specific search strategies for the effective retrieval of healthcare and shopping information. In Proceedings of the ACM SIGCHI Conference on human factors in computing systems (pp. 610–611). ACM Press,
Bhavnani, S.K., Christopher, B.K., Johnson, T.M., Little, R.J., Peck, F.A., Schwartz, J.L., and Strecher, V.J. (2003). Strategy hubs: Next-generation domain portals with search procedures. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 393–400). ACM Press.
Biczok, G., Martinez, D., Jelle, T., and Krogstie, J. (2014). Navigating MazeMap: Indoor human mobility, spatio-logical ties and future potential. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (pp. 266–271). IEEE.
Biedert, R., Buscher, G., and Dengel, A. (2010). The eyeBook – using eye tracking to enhance the reading experience. Informatik Spektrum, 33, 3 (June), 272–281.Google Scholar
Biedert, R., Dengel, A., Buscher, G., and Vartan, A. (2012). Reading and estimating gaze on smart phones. In Proceedings of the symposium on eye tracking research and applications (pp. 385–388). ACM Press.
Bier, E.A., Stone, M.A., Pier, K., Buxton, W., and DeRose, T.D. (1993). Toolglass and magic lenses: The see through interface. In Proceedings of the ACM SIGGRAPH conference on computer graphics and interactive techniques (pp. 73–80). ACM Press.
Bilal, D. (2000). Children's use of the Yahooligans! Web search engine: I. Cognitive, physical, and affective behaviors on fact-based search tasks. Journal of the American Society for information Science, 51(7), 646–665.Google Scholar
Bilenko, M. and Richardson, M. (2011). Predictive client-side profiles for personalized advertising. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 413–421). ACM Press.
Bilenko, M. and White, R.W. (2008). Mining the search trails of surfing crowds: Identifying relevant websites from user activity. In Proceedings of the international conference on the World Wide Web (pp. 51–60). ACM Press.
Billerbeck, B. and Zobel, J. (2004). Techniques for efficient query expansion. In Proceedings of string processing and information retrieval (pp. 30–42). Springer Berlin Heidelberg.
Billsus, D. and Pazzani, M.J. (1999). A personal news agent that talks, learns and explains. In Proceedings of the conference on autonomous agents (pp. 268–275). ACM Press.
Bingham, D. and Hailey, D.J. (1989). The time-urgency component of the type a behavior pattern: time pressure and performance. Journal of Applied Social Psychology, 19, 425–432.Google Scholar
Birchler, V. and Bütler, M. (2007). Information economics. Routledge, 1st edition.
Birnbaum, M.H. (2000). Psychological experiments on the internet. London: Academic Press.
Bishop, C.M. (2006). Pattern recognition and machine learning. New York: Springer.
Bishop, J. (1989). Incentives for learning: Why American high school students compare so poorly to their counterparts overseas (CAHRS Working Paper #89-09). Ithaca, NY: Cornell University, School of Industrial and Labor Relations, Center for Advanced Human Resource Studies. http://digitalcommons.ilr.cornell.edu/cahrswp/400.
Blair, D.C. (1980). Searching biases in large interactive document retrieval systems. Journal of the American Society for Information Science, 31(4), 271–277.Google Scholar
Blair, D.C. and Maron, M.E. (1985). An evaluation of retrieval effectiveness for a full-text document-retrieval system. Communications of the ACM, 28(3), 289–299.Google Scholar
Blattner, M., Sumikawa, D., and Greenberg, R. (1989). Earcons and icons: Their structure and common design principles. Human Computer Interaction, 4(1), 11–44.Google Scholar
Bloom, B.S., Englehard, E., Furst, W., and Krathwohl, D.R. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals. New York: McKay.
Boden, M.A. (1996). Agents and creativity. In Gorayska, G. and Mey, J.L. (Eds.). Cognitive technology: In search of a humane interface. Elsevier Science B.V. (pp. 119–127).
Boden, M.A. (2004). The creative mind: myths and mechanisms. Psychology Press.
Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., and Vigna, V. (2008). The query flow graph: Model and applications. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 609–618). ACM Press.
Bolger, F. and Wright, G. (1992). Reliability and validity in expert judgment. In Wright, G. and Bolger, F. (Eds.), Expertise and decision support. New York: Plenum (pp. 47–76).
Bolt, R.A. (1980). “Put-that-there”: Voice and gesture at the graphics interface. ACM Computer Graphics, 14(3), 262–270. ACM Press.Google Scholar
Bonwell, C.C. and Eison, J.A. (1991). Active Learning: Creating Excitement in the Classroom. Washington, DC: George Washington University, ERIC Clearinghouse on Higher Education.
Borgman, C. (1984). Psychological research in human-computer interaction. Annual Review of Information Science and Technology, 19, 33–64.Google Scholar
Borgman, C.L. (1985). The user's mental model of an information retrieval system. In Proceedings of the ACM SIGIR conference on research and development in informational retrieval (pp. 268–273). ACM Press.
Borgman, C.L. (1986). The user's mental model of an information retrieval system: An experiment on a prototype online catalog. International Journal of Man-Machine Studies, 24(1), 47–64.Google Scholar
Borgman, C.L. (1996). Why are online catalogs hard to use? Lessons learned from information-retrieval studies. Journal of the American Society for Information Science, 37(6), 387–400.Google Scholar
Borlund, P. (2000). Experimental components for the evaluation of interactive information retrieval systems. Journal of documentation, 56(1), 71–90.Google Scholar
Borlund, P. (2003). The IIR evaluation model: A framework for evaluation of interactive information retrieval systems. Information Research, 8(3).Google Scholar
Borlund, P. and Ingwersen, P. (1998). Measures of relative relevance and ranked half-life: Performance indicators for interactive IR. In Proceedings of the ACM SIGIR conference on research and development in informational retrieval (pp. 324–331). ACM Press.
Bos, N., Olson, J., Gergle, D., Olson, G., and Wright, Z. (2002). Effects of four computer-mediated communications channels on trust development. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 135–140). ACM Press.
Bottou, L. (1998). Online Algorithms and Stochastic Approximations, Online Learning and Neural Networks, edited by Saad, David,Cambridge: Cambridge University Press.
Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D.X., Chickering, D.M., Portugaly, E., Snelson, E. (2013). Counterfactual reasoning and learning systems: The example of computational advertising. The Journal of Machine Learning Research, 14(1), 3207–3260.Google Scholar
Boud, D., Keogh, R., and Walker, D. (1985). Reflection: Turning Experience into Learning. London: Kogan Page.
Boyce, A. (1982). Beyond topicality: A two-stage view of relevance and the retrieval process. Information Processing and Management, 18(3), 105–109.Google Scholar
Boyd, D. and Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication and Society, 15(5), 662–679.Google Scholar
Bradner, E., Kellogg, W., and Erickson, T. (1999). The adoption and use of babble: A field study of chat in the workplace. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 139–158). ACM Press.
Brajnik, G., Guida, G., and Tasso, C. (1987). User modeling in intelligent information retrieval. Information Processing and Management, 23(4), 305–320.Google Scholar
Brajnik, G., Mizzaro, S., and Tasso, C. (1996). Evaluating user interfaces to information retrieval systems: A case study of user support. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 128–136). ACM Press.
Brajnik, G., Mizzaro, S., and Tasso, C. (2002). Strategic help in user interfaces for information retrieval. Journal of the American Society for Information Science and Technology, 53(5), 343–358.Google Scholar
Brand-Gruwel, S., Wopereis, I., and Vermetten, Y. (2005). Information problem solving by experts and novices: Analysis of a complex cognitive skill. Computers in Human Behavior, 21, 487–508.Google Scholar
Brandt, J., Guo, P.J., Lewenstein, J., Dontcheva, M., and Klemmer, S.R. (2009). Two studies of opportunistic programming: interleaving web foraging, learning, and writing code. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1589–1598). ACM Press.
Breese, J.S., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the conference on uncertainty in artificial intelligence (pp. 43–52). Morgan Kaufmann Publishers Inc.
Brewer, J.D. (2000). Ethnography. Philadelphia: Open University Press, p.10.
Brewster, S.A., Wright, P.C., and Edwards, A.D.N. (1993). An evaluation of earcons for use in auditory human-computer interfaces. In Proceedings of InterCHI Conference (pp. 222–227). ACM Press
Brewster, S.A., Wright, P.C. and Edwards, A.D.N. (1994). The design and evaluation of an auditory-enhanced scrollbar. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 173–179). ACM Press, Addison-Wesley.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. In Proceedings of the international conference on the World Wide Web (pp. 107–117).
Broder, A. (2002). A taxonomy of web search. SIGIR Forum, 36(2), 3–10. ACM Press.Google Scholar
Broder, A., Churchill, E., Hearst, M., Pell, B., Raghavan, P., and Tomkins, A. (2010). Search is dead!: Long live search (panel). In Proceedings of the international conference on the World Wide Web, (pp. 1337–1338).
Broder, A., Garcia-Pueyo, L., Josifovski, V., Vassilvitskii, S., and Venkatesan, S. (2014). Scalable K-Means by ranked retrieval. In Proceedings of the ACM WSDM conference on Web search and data mining (pp. 233–242). ACM Press.
Bron, M., Van Gorp, J., Nack, F., de Rijke, M., Vishneuski, A., and de Leeuw, S. (2012). A subjunctive exploratory search interface to support media studies researchers. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 425–434). ACM Press.
Bronkhorst, A.W. (2000). The cocktail party phenomenon: A review on speech intelligibility in multiple-talker conditions. Acta Acustica united with Acustica, 86, 117–128.Google Scholar
Brooks, P.Phang, K.Y., Oard, D.W., White, R.W., Bradley, R., and Gumbretière, F. (2006). Measuring the utility of gaze detection for task modeling: A study design. In Proceedings of the workshop on intelligent user interfaces for intelligence analysis.
Brown, I. (1999). Developing a virtual reality user interface (VRUI) for geographic information retrieval on the Internet. Transactions in GIS, 3(3), 207–220.Google Scholar
Burdea, Grigore C., Coiffet., Philippe (2003). Virtual reality technology. Wiley-IEEE Press.
Bruffee, K. (1999). Collaborative learning: Higher education, interdependence, and the authority of knowledge. Baltimore: The Johns Hopkins University Press.
Bruner, J.S. (1961). The act of discovery. Harvard Educational Review, 31, 21–32.Google Scholar
Brünken, R., Steinbacher, S., Plass, J.L., and Leutner, D. (2002). Assessment of cognitive load in multimedia learning using dual-task methodology. Experimental Psychology, 49(2), 109–119.Google Scholar
Brusilovsky, P., Cassel, L., Delcambre, L., Fox, E., Furuta, R., Garcia, D.D., and Yudelson, M. (2010). Enhancing digital libraries with social navigation: The case of ensemble. In Research and Advanced Technology for Digital LibrariesSpringer Berlin Heidelberg (pp. 116–123).
Brusilovsky, P., Chavan, G., and Farzan, R. (2004). Social adaptive navigation support for open corpus electronic textbooks. In Proceedings of international conference on adaptive hypermedia and adaptive Web-based systems (pp. 24–33).
Buchanan, G. and Loizides, F. (2007). Investigating document triage on paper and electronic media. In Proceedings of the European conference on digital libraries, 416–427.
Buckley, C., Salton, G., and Allan, J. (1992). Automatic Retrieval with Locality Information Using Smart. In The first Text REtrieval Conference (TREC-1), National Institute of Standards and Technology, Gaithersburg, MD (pp. 59–72).
Buckley, C. and Voorhees, E.M. (2004). Retrieval evaluation with incomplete information. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 25–32). ACM Press.
Budhu, M. and Coleman, A. (2002). The design and evaluation of interactivities in a digital library. D-Lib Magazine, 8(11).
Budzik, J. and Hammond, K.J. (2000). User interactions with everyday applications as context for justin-in-time information access. In Proceedings of the annual conference on intelligent user interfaces (pp. 44–51).
Bull, S. (2004). Supporting learning with open learner models. In Proceedings of Hellenic conference on information and communication technologies in education (pp. 47–61).
Burdea, G. and Coiffet, P. (2003). Virtual reality technology. Presence: Teleoperators and Virtual Environments, 12(6), 663–664.Google Scholar
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G. (2005). Learning to rank using gradient descent. In Proceedings of the international conference on machine learning (pp. 89–96). ACM Press.
Burke, M., Marlow, C., and Lento, T. (2009). Feed me: Motivating newcomer contribution in social network sites. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 945–954). ACM Press.
Burke, M., and Settles, B. (2011). Plugged in to the community: social motivators in online goal-setting groups. In Proceedings of the international conference on communities and technologies (pp. 1–10). ACM Press.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370.Google Scholar
Burt, R.S. (2005). Brokerage and closure: An introduction to social capital. Oxford University Press: Oxford.
Buscher, G., Dengel, A. and Van Elst, L. (2008). Query expansion using gaze-based feedback on the subdocument level. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 387–394). ACM Press.
Buscher, G., Dumais, S.T., and Cutrell, E. (2010). The good, the bad, and the random: an eye-tracking study of ad quality in web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 42–49). ACM Press.
Buscher, G., van Elst, L., and Dengel, A. (2009). Segment-level display time as implicit feedback: A comparison to eye tracking. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 67–74). ACM Press.
Buscher, G., White, R.W., Dumais, S., and Huang, J. (2012). Large-scale analysis of individual and task differences in search result page examination strategies. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 373–382). ACM Press.
Bush, V. (1945). As we may think. Atlantic Monthly, 3(2), 37–46.Google Scholar
Butler, A., Izadi, S., and Hodges, S. (2008). SideSight: Multi-touch interaction around small devices. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 201–204). ACM Press.
Butler, D. (February 14, 2013). When Google got flu wrong. Nature, 494(7436), 155–156.Google Scholar
Butler, E.S., Aasheim, C., and Williams, S. (2007). Does telecommuting improve productivity?Communications of the ACM, 50(4), 101–103.Google Scholar
Buxton, B. (2007). Multi-touch systems that I have known and loved. Microsoft Research, 56, 1–11.Google Scholar
Byström, K. and Järvelin, K. (1995). Task complexity affects information seeking and use. Information Processing and Management, 31(2), 191–213.Google Scholar
Cacioppo, J.T., and Petty, R.E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116.Google Scholar
Cadez, I., Heckerman, D., Meek, C., Smyth, P., and White, S. (2003). Visualization of navigation patterns on a web site using model based clustering. Data Mining and Knowledge Discovery, 7, 399–424.Google Scholar
Cadoz, C. (1994). Les réalités virtuelles. Dominos, Flammarion.
Callan, J. P. (1994). Passage-level evidence in document retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 302–310). Springer-Verlag New York, Inc.
Callan, J.P., Lu, Z., and Croft, W.B. (1995). Searching distributed collections with inference networks. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 21–28). ACM Press.
Calvo, R.A. and D'Mello, Sidney (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37.Google Scholar
Camerer, C.F. and Johnson, E.J. (1991). The process-performance paradox in expert judgment: How can the experts know so much and predict so badly? In Ericsson, K. A. and Smith, J., eds., Towards a General Theory of Expertise: Prospects and Limits (pp. 195–217). Cambridge: Cambridge University Press.
Campbell, I. (2000). Interactive evaluation of the ostensive model using a new test collection of images with multiple relevance assessments. Information Retrieval, 2(1), 89–114.Google Scholar
Campbell, I. and van Rijsbergen, C.J. (1996). The ostensive model of developing information needs. In Proceedings of the COLIS conference on conceptions of library and information science (pp. 251–268).
Capra, R. and Pérez-Quiñones, M.A. (2005). Using Web search engines to find and refind information. IEEE Computer, 38(10), 36–42.Google Scholar
Capra, R., Arguello, J., Crescenzi, A., and Vardell, E. (2015). Differences in the use of search assistance for tasks of varying complexity. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 23–32). ACM Press.
Caracciolo, C. and de Rijke, M. (2006). Generating and retrieving text segments for focused access to scientific documents. In Proceedings of the European conference on advances in information retrieval (pp. 350–361).
Carbonell, J. and Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 335–336). ACM Press.
Card, S.K., Mackinlay, J. D., and Shneiderman, B. (eds.). (1999). Readings in information visualization: Using vision to think. Morgan Kaufmann.
Card, S.K., Moran, T.P., and Newell, A. (1983). The Psychology of Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum Associates.
Card, S.K., Pirolli, P., Van Der Wege, M., Morrison, J.B., Reeder, R.W., Schraedley, P.K., and Boshart, J. (2001). Information scent as a driver of Web behavior graphs: Results of a protocol analysis method for Web usability. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 498–505). ACM Press.
Card, S.K., Robertson, G.C., and Mackinlay, J.D. (1991). The information visualizer, an information workspace. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 181–188). ACM Press
Card, S. K., Robertson, G.G., and York, W. (1996). The WebBook and the Web forager: An information workspace for the World-Wide Web. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 111-ff). ACM Press.
Cardoso, J.F.C. (1998). Blind signal separation: Statistical principles. In Proceedings of the IEEE, 86(10), 2009–2025.Google Scholar
Carmagnola, F. and Cena, F. (2009). User identification for cross-system personalisation. Information Sciences, 179(1), 16–32.Google Scholar
Carmel, E., Crawford, S., and Chen, H. (1992). In browsing in hypertext: A cognitive study. In Proceedings of the IEEE transactions on systems, man and cybernetics (pp. 865–884).
Carrington, P.J., Scott, J., and Wasserman, S. (Eds.). (2005). Models and Methods in Social Network Analysis. Cambridge, MA: Cambridge University Press.
Carroll, J.M. and Anderson, N.S. (1987). Mental models in human-computer interaction: Research issues about what the user of software knows (No. 12). Olson, J. R. (Ed.). National Academies.
Carroll, J.M. and Rosson, M.B. (1992). Getting around the task-artifact cycle: how to make claims and design by scenario. ACM Transactions on Information Systems, 10(2), 181–212. ACM Press.Google Scholar
Carroll, J.M. and Thomas, J.C. (1988). Fun. SIGCHI Bulletin, 19(3), 21–24. ACM Press.Google Scholar
Carta, T., Paternò, F., and Santana, V. (2011). Support for remote usability evaluation of web mobile applications. In Proceedings of the ACM international conference on design of communication (pp. 129–136). ACM Press.
Carterette, B. and Jones, R. (2007). Evaluating search engines by modeling the relationship between relevance and clicks. In Proceedings of the conference on advances in neural information processing systems (pp. 217–224).
Cartright, M.A., White, R.W., and Horvitz, E. (2011). Intentions and attention in exploratory health search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 65–74). ACM Press.
Case, D.O. (2002). Looking for Information: A Survey of Research on Information Seeking, Needs, and Behavior. London: Academic Press.
Castillo, C., Gionis, A., Lempel, R., and Maarek, Y. (2010). When no clicks are good news. Presentation at the SIGIR 2010 industry day. http://www.eurospider.com/fileadmin/pdf/SIGIR_Industry_Track_2010/11_SIGIR-2010-CASTILLO.pdf. Accessed on August 16, 2015.
Catledge, L.D. and Pitkow, J.E. (1995). Characterizing browsing strategies in the World-Wide Web. Computer Networks and ISDN systems, 27(6), 1065–1073.Google Scholar
Ceaparu, I., Lazar, J., Bessiere, K., Robinson, J., and Shneiderman, B. (2004). Determining causes and severity of end-user frustration. International Journal of Human-Computer Interaction, 17(3), 333–356.Google Scholar
Cegarra, J. and Chevalier, A. (2008). The use of tholos software for combining measures of mental workload: Toward theoretical and methodological improvements. Behavior Research Methods, 40(4), 988–1000.Google Scholar
Chakrabarti, S., Frieze, A., and Vera, J. (2005). The influence of search engines on preferential attachment. In Proceedings of the ACM-SIAM symposium on discrete algorithms (pp. 293–300). Society for Industrial and Applied Mathematics.
Chaiken, R., Jenkins, B., Larson, P.Å., Ramsey, B., Shakib, D., Weaver, S., and Zhou, J. (2008). SCOPE: Easy and efficient parallel processing of massive data sets. In Proceedings of the VLDB Endowment, 1(2), 1265–1276.Google Scholar
Chalmers, M., Rodden, K., and Brodbeck, D. (1998). The order of things: Activity-centred information access. Computer Networks and ISDN Systems, 30(1), 359–367.Google Scholar
Chan, D., Ge, R., Gershony, O., Hesterberg, T., and Lambert, D. (2010). Evaluating online ad campaigns in a pipeline: Causal models at scale. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 7–16). ACM Press.
Chan, R.C.K., Shum, D., Toulopoulou, T., Chen, E.Y.H., R., Shum, D., Toulopoulou, T., and Chen, E. (2008). Assessment of executive functions: Review of instruments and identification of critical issues. Archives of Clinical Neuropsychology, 23(2), 201–216.Google Scholar
Chang, S. and Rice, R. (1993). Browsing: A multidimensional framework. Annual review of information science and technology (ARIST), 28, 231–276.Google Scholar
Chapelle, O., Joachims, T., Radlinski, F., and Yue, Y. (2012). Large scale validation and analysis of interleaved search evaluation. ACM Transactions on Information Systems, 30(1), 6.Google Scholar
Chapelle, O., Metlzer, D., Zhang, Y., and Grinspan, P. (2009). Expected reciprocal rank for graded relevance. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 621–630). ACM Press.
Chapelle, O. and Zhang, Y. (2009). A dynamic bayesian network click model for web search ranking. In Proceedings of the international conference on World Wide Web (pp. 1–10). ACM Press.
Charnov, E.L. (1976). Optimal foraging, the marginal value theorem. Theoretical Population Biology, 9, 129–136.Google Scholar
Chase, W.G. and Simon, H.A. (1973). The mind's eye in chess. In Chase, W.G. (Ed.), Visual Information Processing. New York: Academic Press, pp. 215–281.
Chau, M. and Betke, M. (2005). Real Time Eye Tracking and Blink Detection with USB Cameras. Boston, MA: Boston University Computer Science Department.
Chaudhuri, S. and Kaushik, R. (2009). Extending autocompletion to tolerate errors. In Proceedings of the ACM SIGMOD conference on management of data (pp. 707–718). ACM Press.
Chawla, S., Hartline, J.D., and Sivan, B. (2012). Optimal crowdsourcing contests. In Proceedings of the ACM SIAM symposium on discrete algorithms (pp. 856–868). SIAM.
Chen, G., and Chiu, M.M. (2008). Online discussion processes. Computers and Education, 50, 678–692.Google Scholar
Chen, H. and Karger, D.R. (2006). Less is more: Probabilistic models for retrieving fewer relevant documents. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 429–436). ACM Press.
Chen, J. and Ji, Q. (2015). A probabilistic approach to online eye gaze tracking without explicit personal calibration. IEEE Transactions on Image Processing, 24(3), 1076–1086.Google Scholar
Cheng, Z., Caverlee, J., Barthwal, H., and Bachani, V. (2014). Who is the barbecue king of Texas?: A geo-spatial approach to finding local experts on Twitter. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 335–344). ACM Press.
Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C. (2000). Developing a context-aware electronic tourist guide: Some issues and experiences. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 17–24). ACM Press.
Chi, E.H. (2009). Information seeking can be social. IEEE Computer, 42(3), 42–46.
Chi, E.H. (2012). Who knows?: Searching for expertise on the social web. Communications of the ACM, 55(4), 110–110.Google Scholar
Chi, E.H., Hong, L., Gumbrecht, M., and Card, S.K. (2005). ScentHighlights: Highlighting conceptually-related sentences during reading. In Proceedings of the ACM IUI conference on intelligent user interfaces (pp. 272–274). ACM Press.
Chi, E. H., Pirolli, P., Chen, K., and Pitkow, J. (2001). Using information scent to model user information needs and actions and the Web. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 490–497). ACM Press.
Chi, E.H., Pirolli, P., and Lam, S.K. (2007). Aspects of augmented social cognition: Social information foraging and social search. In Proceedings of Online Communities and Social Computing (pp. 60–69). Springer Berlin Heidelberg.
Chi, E.H., Pirolli, P., and Pitkow, J. (2000). The scent of a site: A system for analyzing and predicting information scent, usage, and usability of a web site. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 161–168). ACM Press.
Chi, E.H., Rosien, A., Supattanasiri, G., Williams, A., Royer, C., Chow, C., and Cousins, S. (2003). The bloodhound project: Automating discovery of web usability issues using the InfoScent simulator. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 505–512). ACM Press.
Chi, M.T., Glaser, R., and Farr, M.J. (Eds.). (2014). The Nature of Expertise. Psychology Press.
Chi, M.T.H., Glaser, R., and Rees, E. (1982). Expertise in problem solving. In Sternberg, R.S. (Ed.), Advances in the Psychology of Human Intelligence. Hillsdale, NJ Erlbaum, Vol. 1 (pp. 1–75).
Chierichetti, F., Kumar, R., and Raghavan, P. (2011). Optimizing two-dimensional search results presentation. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 257–266). ACM Press.
Chierichetti, F., Kumar, R., and Tomkins, A. (2010). Stochastic models for tabbed browsing. In Proceedings of the international conference on World Wide Web (pp. 241–250). ACM Press.
Chirita, P.A., Nejdl, W., Paiu, R., and Kohlschütter, C. (2005). Using ODP metadata to personalize search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 178–185). ACM Press.
Cho, J. and Roy, S. (2004). Impact of search engines on page popularity. In Proceedings of the international conference on the World Wide Web (pp. 20–29). ACM Press.
Choi, H., and Varian, H. (2012). Predicting the present with google trends. Economic Record, 88(s1), 2–9.Google Scholar
Chon, Y., Lane, N.D., Li, F., Cha, H., and Zhao, F. (2012). Automatically characterizing places with opportunistic crowdsensing using smartphones. In Proceedings of the ACM conference on ubiquitous computing (pp. 481–490). ACM Press.
Choo, C.W., Detlor, B., and Turnbull, D. (2000). Information seeking on the Web: An integrated model of browsing and searching. First Monday, 5(2).Google Scholar
Chu, W. and Keerthi, S.S. (2005). New approaches to support vector ordinal regression. In Proceedings of the international conference on machine learning (pp. 145–152). ACM Press.
Chun, M.M. and Potter, M.C. (1995). A two-stage model for multiple target detection in rapid serial visual presentation. Journal of Experimental Psychology: Human Perception and Performance, 21(1), 109.Google Scholar
Churchill, E.F. and Munro, A.J. (2001). Work/place: Mobile technologies and arenas of activity. ACM SigGroup Bulletin, 22(3), 3–9.Google Scholar
Clark, H.H. and Brennan, S.E. (1991). Grounding in communication. In Resnick, L.B., Levine, J.M., and Teasley, S.D. (eds.). Perspectives on Socially Shared Cognition (pp. 127–149). American Psychological Association.
Clarke, C.L.A., Agichtein, E., Dumais, S.T., and White, R.W. (2007). The influence of caption features on clickthrough patterns in web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 135–142). ACM Press.
Clarke, C.L., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., and MacKinnon, I. (2008). Novelty and diversity in information retrieval evaluation. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 659–666). ACM Press.
Claypool, M., Le, P., Waseda, M., and Brown, D. (2001). Implicit interest indicators. In Proceedings of the ACM IUI on intelligent user interfaces (pp. 33–40). ACM Press.
Cleverdon, C.W. (1970). The effect of variations in relevance assessments in comparative experimental tests of index languages. Cranfield: Cranfield Institute of Technology. (Cranfield Library Report No. 3)
Cleverdon, C.W., Mills, J., and Keen, E.M. (1966). Factors determining the performance of indexing systems. Cranfield: Aslib Cranfield Research Project, College of Aeronautics. (Vol. 1: Design; Vol. 2: Results)
Clough, P., Ford, N., and Stevenson, M. (2011). Personalizing access to cultural heritage collections using pathways. In Proceedings of workshop on personalized access to cultural heritage.
Cockburn, A. and McKenzie, B.. (2001). What do Web users do? An empirical analysis of Web use. International Journal of Human-Computer Studies, 54(6), 903–922.Google Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
Cohen, N. (2010). “Suicide” query prompts Google to offer hotline. In New York Times, April 2010.
Cohen, W.W., Shapire, R.E., and Singer, Y. (1999). Learning to order things. Journal of Artificial Intelligence Research, 10, 243–270.Google Scholar
Cohn, G., Morris, D., Patel, S., and Tan, D. (2012). Humantenna: Using the body as an antenna for real-time whole-body interaction. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1901–1910). ACM Press.
Cohn, G., Morris, D., Patel, S.N., and Tan, D.S. (2011). Your noise is my command: Sensing gestures using the body as an antenna. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 791–800). ACM Press.
Cole, M.J., Gwizdka, J., Liu, C., Belkin, N.J., and Zhang, X. (2013). Inferring user knowledge level from eye movement patterns. Information Processing and Management, 49(5), 1075–1091.Google Scholar
Cole, M.J., Gwizdka, J., Liu, C., Bierig, R., Belkin, N.J., and Zhang, X. (2011). Task and user effects on reading patterns in information search. Interacting with Computers, 23(4), 346–362.Google Scholar
Cole, M.J., Hendahewa, C., Belkin, N.J., and Shah, C. (2014). Discrimination between tasks with user activity patterns during information search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 567–576). ACM Press.
Collins, A., Brown, J.S., and Newman, S.E. (1989). Cognitive apprenticeship: teaching the crafts of reading, writing, and mathematics. Knowing, learning, and instruction: Essays in honor of Robert Glaser, 18, 32–42.Google Scholar
Collins-Thompson, K. (2009a). Accounting for stability of retrieval algorithms using risk-reward curves. In Proceedings of the SIGIR workshop on the future of IR evaluation (pp. 27–28).
Collins-Thompson, K. (2009b). Reducing the risk of query expansion via robust constrained optimization. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 837–846). ACM Press.
Collins-Thompson, K., Bennett, P.N., White, R.W., de la Chica, S., and Sontag, D. (2011). Personalizing web search results by reading level. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 403–412). ACM Press.
Comon, P. (1994). Independent component analysis: A new concept?Signal Processing, 36(3): 287–314.Google Scholar
Condie, T., Conway, N., Alvaro, P., Hellerstein, J.M., Elmeleegy, K., and Sears, R. (2010). MapReduce online. In Proceedings of Symposium on Networked Systems Design and Implementation, 10(4), 20.Google Scholar
Cong, G., Wang, L., Lin, C.Y., Song, Y.I., and Sun, Y. (2008). Finding question-answer pairs from online forums. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 467–474). ACM Press.
Consolvo, S., McDonald, D., and Landay, J. (2009). Theory-driven design strategies for technologies that support behavior change in everyday life. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 405–414). ACM Press.
Convertino, G., Mentis, H.M., Rosson, M.B., Slavkovic, A., and Carroll, J.M. (2009). Supporting content and process common ground in computer-supported teamwork. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2339–2348). ACM Press.
Cool, C. (2001). The concept of situation in information science. Annual review of information science and technology, 35, 5–42.Google Scholar
Cool, C. and Belkin, N.J. (2002) A classification of interactions with information. In Proceedings of the COLIS conference on conceptions of library and information science (pp. 1–15).
Cool, C., Park, S., Belkin, N.J., Koenemann, J., and Ng, K.B. (1996). Information seeking behavior in new searching environment In Proceedings of the COLIS conference on conceptions of library and information science (pp. 403–416).
Cool, C. and Spink, A. (2002). Issues of context in information retrieval (IR): An introduction to the special issue. Information Processing and Management, 38(5), 605–611.Google Scholar
Cooper, A. (2008). A survey of query log privacy-enhancing techniques from a policy perspective. ACM Transactions on the Web, 2(4), 19.Google Scholar
Cooper, D.G., Arroyo, I., Woolf, B.P., Muldner, K., Burleson, W., and Christopherson, R. (2009). Sensor model student self-concept in the classroom. In Proceedings of the conference on user modeling and personalization (pp. 30–41).
Cooper, M.D. (1972). A cost model for evaluating information retrieval systems. Journal of the American Society for Information Science, 23(5), 306–312.Google Scholar
Cooper, W. (1968). Expected search length: A single measure of retrieval effectiveness based on the weak ordering action of retrieval systems. American Documentation, 19(1), 30–41.Google Scholar
Cooper, W.S., Gey, F.C., and Dabney, D.P. (1992). Probabilistic retrieval based on staged logistic regression. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 198–210). ACM Press.
Corbett, A.T., Koedinger, K.R., and Anderson, J.R. (1997). Intelligent tutoring systems. Handbook of Human–Computer Interaction (pp. 849–874).
Corston-Oliver, S., Ringger, E., Gamon, M., and Campbell, R. (2004). Task-focused summarization of email. In Proceedings of the ACL workshop: text summarization branches out (pp. 43–50).
Cosijn, E. and Ingwersen, P. (2000). Dimensions of relevance. Information Processing and Management, 36(4), 533–550.Google Scholar
Cranor, L. (2007). Making privacy disclosures to consumers more usable. Bureau of Consumer Protection.
Craswell, N. and Szummer, M. (2007). Random walks on the click graph. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 239–246). ACM Press.
Craswell, N., Zoeter, O., Taylor, M., and Ramsey, B. (2008). An experimental comparison of click position-bias models. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 87–94). ACM Press.
Crawford, K. and Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. Boston College Law Review, 55, 93.Google Scholar
Crescenzi, A., Capra, R., and Arguello, J. (2013). Time pressure, user satisfaction and task difficulty. American Society for Information Science and Technology, 50(1), 1–4.Google Scholar
Crescenzi, A. (2015). Time pressure in information search. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1050–1055). ACM Press.
Croft, W.B., Metzler, D., and Strohman, T. (2010). Search engines: Information retrieval in practice. Reading: Addison-Wesley.
Croft, W.B. and Thompson, R.H. (1987). I3R: A new approach to the design of document retrieval systems. Journal of the American Society for Information Science, 38(6), 389–404.Google Scholar
Cronen-Townsend, S., Zhou, Y., and Croft, W.B. (2002). Predicting query performance. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 299–306). ACM Press.
Cropley, A.J. (2000). Defining and measuring creativity: Are creativity tests worth using?Roeper Review, 23(2), 72–79.Google Scholar
Csikszentmihalyi, M. (1991). Flow: The Psychology of Optimal Experience. New York: Harper-Perennial.
Csikszentmihalyi, M. (1997). Flow and the Psychology of Discovery and Invention. New York: Harper-Perennial.
Cucerzan, S. and Brill, E. (2004). Spelling correction as an iterative process that exploits the collective knowledge of Web users. In Proceedings of the conference on empirical methods in natural language processing (pp. 293–300).
Cui, H., Wen, J.R., Nie, J.Y., and Ma, W. (2002). Probabilistic query expansion using query logs. In Proceedings of the international conference on the World Wide Web (pp.325–332).
Culliss, G. (1999). User popularity ranked search engines. http://web.archive.org/web/20000302121422/http://www.infonortics.com/searchengines/boston1999/culliss/index.htm.
Cutrell, E., Dumais, S.T., and Teevan, J. (2006). Searching to eliminate personal information management. Communications of the ACM, 49(1), 58–64.Google Scholar
Cutrell, E. and Guan, Z. (2007). What are you looking for?: An eye-tracking study of information usage in web search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 407–416). ACM Press.
Cutting, D.R., Karger, D.R., Pedersen, J.O., and Tukey, J.W. (1992). Scatter/gather: A cluster-based approach to browsing large document collections. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 318–329). ACM Press.
Czerwinski, M., Horvitz, E., and Cutrell, E. (2001). Subjective duration assessment: An implicit probe for software usability. In Proceedings of IHM-HCI conference (pp. 167–170).
Czerwinski, M., Cutrell, E., and Horvitz, E. (2000). Instant messaging and interruptions: Influence of task type on performance. In Proceedings of OZCHI (pp. 356–361).
Czerwinski, M., Gage, D., Gemmell, J., Marshall, C., Perez-Quinonesis, M., Skeels, M., and Catarci, T. (2006). Digital memories in an era of ubiquitous computing and abundant storage. Communications of the ACM, 49(1), 44–50.Google Scholar
Czerwinski, M. and Horvitz, E. (2002). An investigation of memory for daily computing events. In Proceedings of People and Computers XVI-Memorable Yet Invisible (pp. 229–245). Springer London.
Czerwinski, M., Horvitz, E., and Cutrell, E. (2001). Subjective duration assessment: An implicit probe for software usability. In Proceedings of IHM-HCI 2001 conference (pp. 167–170).
Czerwinski, M., Horvitz, E., and Wilhite, S. (2004). A diary study of task switching and interruptions. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 175–182). ACM Press.
Dan, O., Dmitriev, P., and White, R.W. (2012). Mining for insights in the search engine query stream. In Proceedings of the international conference companion on the World Wide Web (pp. 489–490). ACM Press.
Dang, V. and Croft, W.B. (2010). Query reformulation using anchor text. In Proceedings of the ACM WSDM conference on Web search and data mining (pp. 41–50). ACM Press.
Daoud, L.Tamine-Lechani, L., Boughanem, M., and Chebaro, B. (2009). A session based personalized search using an ontological user profile. In Proceedings of the ACM symposium on applied computing (pp. 1732–1736). ACM Press.
Das Sarma, A., Gollapudi, S., and Ieong, S. (2008). Bypass rates: Reducing query abandonment using negative inferences. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 177–185). ACM Press.
Dasgupta, A., Gurevich, M., Zhang, L., Tseng, B., and Thomas, A.O. (2012). Overcoming browser cookie churn with clustering. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 83–92). ACM Press.
Daumé, H. and Brill, E. (2004). Web search intent induction via automatic query reformulation. In Proceedings of the conference of the north american chapter of the association for computational linguistics – human language technologies: short papers (pp. 49–52). Association for Computational Linguistics.
Davenport, T.H. and Beck, J.C. (2013). The Attention Economy: Understanding the New Currency of Business. Cambridge, MA: Harvard Business Press.
Davies, G. and Thomson, D., eds. (1988). Memory in Context: Context in Memory. Wiley: England.
Davis, S.F. and Palladino, J.J. (1995). Psychology. Englewood Cliffs, NJ: Prentice Hall.
Dean, J. and Barroso, L.A. (2013). The tail at scale. Communications of the ACM, 56(2), 74–80.Google Scholar
Dean, J. and Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.CrossRefGoogle Scholar
Dean-Hall, A., Clarke, C.L.A., Kamps, J., Thomas, P., Simone, N., and Voorhees, E. (2013). Overview of the trec 2013 contextual suggestion track. University of Waterloo (Ontario).
De Brouwer, S., Missal, M., Barnes, G., and Lefèvre, P. (2002). Quantitative analysis of catch-up saccades during sustained pursuit. Journal of Neurophysiology, 87(4), 1772–1780.Google Scholar
De Choudhury, M., Morris, M.R., and White, R.W. (2014). Seeking and sharing health information online: Comparing search engines and social media. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1365–1376). ACM Press.
de Jong, F.P. and Simons, R.J. (1988). Self-regulation in text processing. European Journal of Psychology of Education, 3(2), 177–190.Google Scholar
De Vries, A. P., Kazai, G., and Lalmas, M. (2004). Tolerance to irrelevance: A user-effort oriented evaluation of retrieval systems without predefined retrieval unit. In Proceedings of the conference on computer-assisted information retrieval (RIAO) (pp. 463–473).
Deci, E.L., Koestner, R., and Ryan, R.M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–668.Google Scholar
Dennis, S., McArthur, R., and Bruza, P.D. (1998). Searching the World Wide Web made easy? The cognitive load imposed by query refinement mechanisms. In Proceedings of the Australian document computing symposium (pp. 65–71).
Dennis, S., Bruza, P., and McArthur, R. (2002). Web searching: A process-oriented experimental study of three interactive search paradigms. Journal of the American Society for Information Science and Technology, 53(2), 120–133.Google Scholar
Dervin, B. (1983). An overview of sense-making research: Concepts, methods, and results to date. In Proceedings of the annual meeting of the international communication association, Dallas, TX.
Dervin, B. (1992). From the mind's eye of the user: The sense-making qualitative-quantitative methodology. Qualitative research in information management, 61, 84.Google Scholar
Dervin, B. (1998). Sense-making theory and practice: An overview of user interests in knowledge seeking and use. Journal of knowledge management, 2(2), 36–46.Google Scholar
Dervin, B. and Nilan, M. (1986). Information needs and uses. Annual review of information science and technology, 21, 3–33.Google Scholar
Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., and Tatem, A.J. (2014). Dynamic population mapping using mobile phone data. In Proceedings of the National Academy of Sciences, 111(45), 15888–15893.Google Scholar
De Vries, A.P., Kazai, G., and Lalmas, M. (2004). Tolerance to irrelevance: A user-effort oriented evaluation of retrieval systems without predefined retrieval unit. In Proceedings of RIAO (pp. 463–473).
Dewey, J. (1933). How We Think. Boston: D.C. Heath.
Diaz, F. (2007). Performance prediction using spatial autocorrelation. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 583–590). ACM Press.
Diaz, F., White, R., Buscher, G., and Liebling, D. (2013). Robust models of mouse movement on dynamic web search results pages. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1451–1460). ACM Press.
Dieberger, A. (1997). Supporting social navigation on the World Wide Web. International Journal of Human-Computer Studies, 46(6), 805–825.Google Scholar
Dieberger, A., Dourish, P., Höök, K., Resnick, P., and Wexelblat, A. (2000). Social navigation: Techniques for building more usable systems. Interactions, 7(6), 36–45.Google Scholar
DiGioia, P. and Dourish, P. (2005). Social navigation as a model for usable security. In Proceedings of the symposium on usable privacy and security (pp. 101–108). ACM Press.
Dillon, A. and Watson, C. (1996). User analysis in HCI: The historical lessons from individual differences research. International Journal of Human-Computer Studies, 45(6), 619–637.Google Scholar
Dincer, B.T., Macdonald, C., and Ounis, I. (2014). Hypothesis testing for the risk-sensitive evaluation of retrieval systems. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 23–32). ACM Press.
Dix, A.J. and Beale, R. (1996). Remote Cooperation: CSCW Issues for Mobile and Teleworkers. New York: Springer-Verlag.
Diriye, A., White, R., Buscher, G., and Dumais, S. (2012). Leaving so soon? Understanding and predicting web search abandonment rationales. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1025–1034). ACM Press.
Dolin, R.A. (2010). Search query privacy: The problem of anonymization. Hastings Science and Technology Law Journal, 2, 137.Google Scholar
Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87.Google Scholar
Donath, J. and Robertson, N. (1994). The sociable web. In Proceedings of the annual international World Wide Web conference.
Donato, D., Bonchi, F., Chi, T., and Maarek, Y. (2010). Do you want to take notes?: Identifying research missions in Yahoo! search pad. In Proceedings of the international conference on the World Wide Web (pp. 321–330). ACM Press.
Dong, A., Zhang, R., Kolari, P., Bai, J., Diaz, F., Chang, Y., Zheng, Z., and Zha, H. (2010). Time is of the essence: Improving recency ranking using twitter data. In Proceedings of the international conference on the World Wide Web (pp. 331–340). ACM Press.
Dontcheva, M., Drucker, S., Wade, G., Salesin, D., and Cohen, M. (2006). Summarizing personal web ubrowsing sessions. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 115–124). ACM Press.
Dontcheva, M., Drucker, S.M., Salesin, D., and Cohen, M.F. (2007). Relations, cards, and search templates: User-guided web data integration and layout. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 61–70). ACM Press.
Dörk, M., Carpendale, S., and Williamson, C. (2011). The information flaneur: A fresh look at information seeking. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1215–1224). ACM Press.
Dou, Z., Song, R., and Wen, J.R. (2007). A large-scale evaluation and analysis of personalized search strategies. In Proceedings of the international conference on the World Wide Web (pp. 581–590). ACM Press.
Dourish, P. and Chalmers, M. (1994). Running out of space: Models of information navigation. In Proceedings of the human-computer interaction conference (pp. 23–26).
Dourish, P. and Bellotti, V. (1992). Awareness and coordination in shared workspaces. In Proceedings of the ACM CSCW conference on computer-supported cooperative work (pp. 107–114). ACM Press.
Downey, D., Dumais, S., Liebling, D., and Horvitz, E. (2008). Understanding the relationship between searchers' queries and information goals. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 449–458). ACM Press.
Downey, D., Dumais, S.T., and Horvitz, E. (2007). Models of searching and browsing: Languages, studies, and application. In Proceedings of the international joint conference on artificial intelligence (pp. 2740–2747).
Dreyfus, S.E. and Dreyfus, H.L. (1980). A five-stage model of the mental activities involved in directed skill acquisition (No. ORC-80-2). California Univ Berkeley Operations Research Center.
Drewes, H. and Schmidt, A. (2007). Interacting with the computer using gaze gestures. In Proceedings of Human-Computer Interaction–INTERACT 2007 (pp. 475–488). Springer Berlin Heidelberg.
Duggan, G.B. and Payne, S.J. (2008). Knowledge in the head and on the web: Using topic expertise to aid search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 39–48). ACM Press.
Dumais, S., Banko, M., Brill, E., Lin, J., and Ng, A. (2002). Web question answering: Is more always better? In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 291–298). ACM Press.
Dumais, S.T. and Belkin, N.J. (2005). The TREC interactive tracks: Putting the user into search. In Voorhees, E.M. and Harman, D. (Eds.). TREC: Experiment and evaluation in information retrieval (pp. 123–152). Cambridge: MIT Press.
Dumais, S.T., Buscher, G., and Cutrell, E. (2010). Individual differences in gaze patterns for web search. In Proceedings of the IIiX symposium on information interaction in context (pp. 185–194). ACM Press.
Dumais, S.T., Cutrell, E., Cadiz, J.J., Jancke, G., Sarin, R., and Robbins, D.C. (2003). Stuff I've seen: A system for personal information retrieval and re-use. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 72–79). ACM Press.
Dumais, S., Cutrell, E., and Chen, H. (2001). Optimizing search by showing results in context. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 277–284). ACM Press.
Dumais, S., Cutrell, E., Sarin, R., and Horvitz, E. (2004). Implicit queries (IQ) for contextualized search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 594–594). ACM Press.
Dumais, S., Jeffries, R., Russell, D. M., Tang, D., and Teevan, J. (2014). Understanding user behavior through log data and analysis. In Olson, J.S. and Kellogg, W.A. (Eds.). Ways of Knowing in HCI (pp. 349–372), New York, NY: Springer.
Dumas, J.S. and Redish, J.C. (1999). A Practical Guide to Usability Testing. Bristol: Intellect Books.
Duncker, K. and Lees, L.S. (1945). On problem solving. Psychological Monographs, 58(5), i.Google Scholar
Dunlop, M. (1997). Time, relevance and interaction modelling for information retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 206–213). ACM Press.
Dupret, G.E. and Piwowarski, B. (2008). A user browsing model to predict search engine click data from past observations. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 331–338). ACM Press.
Dwork, C. (2006). Differential privacy. In Proceedings of the international colloqium on automata, languages, and programming (pp. 1–12). Springer Verlag Heidelberg.
Dye, J. (2009). Consumer privacy advocates seek search engine solution. EContent. March 2009. http://www.econtentmag.com/Articles/News/News-Feature/Consumer-Privacy-Advocates-Seek-Search-Engine-Solution-52679.htm. Accessed on August 15, 2015.
Eckles, D., Karrer, D., and Ugander, J. (2015). Design and analysis of experiments in networks: Reducing bias from interference. (forthcoming)
Edelman, B. (2004). Earnings and Ratings at Google Answers. Unpublished Manuscript.
Edmonds, A., White, R.W., Morris, D., and Drucker, S.M. (2007). Instrumenting the dynamic Web. Journal of Web Engineering, 6(3), 243–260.Google Scholar
Efthimiadis, E.N. (1993). A user-centered evaluation of ranking algorithms for interactive query expansion. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 146–159). ACM Press.
Efthimiadis, E.N. (1996). Query expansion. Annual Review of Information Systems and Technology, 31, 121–187.Google Scholar
Efthimiadis, E.N. and Hendry, D.G. (2005). Search engines and how students think they work. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 595–596). ACM Press.
Egan, D.E., Remde, J.R., Gomez, L.M., Landauer, T.K., Eberhardt, J., and Lochbaum, C.C. (1989). Formative design evaluation of superbook. ACM Transactions on Information Systems, 7(1), 30–57Google Scholar
Ehmke, C., and Wilson, S. (2007). Identifying web usability problems from eye-tracking data. In Proceedings of the British HCI group annual conference on people and computers (pp. 119–128). British Computer Society.
Eickhoff, C., Collins-Thompson, K., Bennett, P., and Dumais, S. (2013). Designing human-readable user profiles for search evaluation. In Proceedings of the European conference on information retrieval (pp. 701–705). Springer-Verlag.
Eickhoff, C., Dungs, S., and Tran, V. (2015). An eye-tracking study of query reformulation. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 13–22). ACM Press.
Eickhoff, C., Teevan, J., White, R., and Dumais, S. (2014). Lessons from the journey: A query log analysis of within-session learning. In Proceedings of the ACM WSDM conference on Web search and data mining (pp. 223–232). ACM Press.
Eiron, N. and McCurley, K.S. (2003). Analysis of anchor text for web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 459–460). ACM Press.
Eisenberg, E.M. (1990). Jamming: Transcendence through organizing. Communication Research 17(2), 139–164.Google Scholar
Eisenberg, M.B. and Berkowitz, R.E. (1990). Information Problem Solving: The Big Six Skills Approach to Library and Information Skills Instruction. Norwood, NJ: Ablex Publishing Corporation.
Eisenberg, M.B. and Berkowitz, R.E. (1992). Information problem-solving: The big six skills approach. School Library Media Activities Monthly, 8(5), 27.Google Scholar
Ekman, P. and Friesen, W.V. (1976). Measuring facial movement. Environmental Psychology and Nonverbal Behavior, 1(1), 56–75.Google Scholar
Elliott, R. (2003). Executive functions and their disorders. British Medical Bulletin, 65, 49–59.Google Scholar
Ellis, D. (1989). A behavioural approach to information retrieval system design. Journal of Documentation, 45(3), 171–212.Google Scholar
Ellis, D. (1993). Modeling the information-seeking patterns of academic researchers: A grounded theory approach. The Library Quarterly, 469–486.
Ellis, D. and Haugan, M. (1997). Modelling the information seeking patterns of engineers and research scientists in an industrial environment. Journal of Documentation, 53(4), 384–403.Google Scholar
Ellis, H.C. (1965). The Transfer of Learning. New York: The Macmillan Company.
Elkahky, A.M., Song, Y., and He, X. (2015). A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the international conference on the World Wide Web (pp. 278–288). International World Wide Web Conferences Steering Committee.
Elsas, J.L. and Dumais, S.T. (2010). Leveraging temporal dynamics of document content in relevance ranking. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 1–10). ACM Press.
Elsweiler, D., Wilson, M.L., and Lunn, B.K. (2011). Understanding casual-leisure information behaviour. Library and Information Science, 1, 211–241.CrossRefGoogle Scholar
Engelbart, D. (1962). Augmenting human intellect: A conceptual framework. Summary Report AFOSR-3233. Menlo Park, CA: Stanford Research Institute.
Epstein, R. and Robertson, R.E. (2015). The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections. Proceedings of the National Academy of Sciences, 112(33), 512–521.Google Scholar
Eraut, M. (1994). Developing Professional Knowledge and Competence. London: Falmer Press.
Erdelez, S. (1999). Information encountering: It's more than just bumping into information. Bulletin of the American Society for Information Science, 25(3), 25–29.
Erdelez, S. (2004). Investigation of information encountering in the controlled research environment. Information Processing and Management, 40(6), 1013–1025.
Ericsson, K.A. (2006). The influence of experience and deliberate practice on the development of superior expert performance. In Ericsson, K.A., Charness, N., Feltovich, P.J., and Hoffman, R.R. (Eds.). The Cambridge Handbook of Expertise and Expert Performance (pp. 683–703). Cambridge University Press.
Ericsson, K.A., Krampe, R.T., and Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363.Google Scholar
Ericsson, K.A. and Smith, J. (Eds.). (1991). Toward a General Theory of Expertise: Prospects and Limits. Cambridge: Cambridge University Press.
Etzioni, O., Banko, M., and Cafarella, M.J. (2006). Machine reading. In Proceedings of the AAAI conference on artificial intelligence (pp. 1517–1519). AAAI Press.
Eugster, M.J., Ruotsalo, T., Spapé, M.M., Kosunen, I., Barral, O., Ravaja, N., and Kaski, S. (2014). Predicting term-relevance from brain signals. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 425–434). ACM Press.
European Parliament. (1995). Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data.
Evans, B.M. and Chi, E.H. (2008). Towards a model of understanding social search. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 485–494). ACM Press.
Eysenbach, G. (2006). Infodemiology: Tracking flu-related searches on the web for syndromic surveillance. In Proceedings of the annual symposium of the American medical informatics association (pp. 244–248).
Eysenbach, G. and Köhler, C. (2002). How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews. British Medical Journal, 324(7337), 573.Google Scholar
Eysenck, H. (1991). Dimensions of personality: 16: 5 or 3? Criteria for a taxonomic paradigm. Personality and Individual Differences, 12, 773–790.Google Scholar
Faber, D. (2006). Google's Marissa Mayer: Speed wins. ZDNet. November 9, 2006.
Fails, J.A. and Olsen, D.R.J. (2003). Interactive machine learning. In Proceedings of the international conference on intelligent user interfaces (pp. 39–45). ACM Press.
Fanelli, G., Weise, T., Gall, J., and Van Gool, L. (2011). Real time head pose estimation from consumer depth cameras. Pattern Recognition (pp. 101–110). Springer Berlin Heidelberg.
Farago, J.H., Williams, H.E., Walsh, J.E., Whyte, N.A., Goel, K.J., Fung, P., and Ray, E.N. (2010). Object search UI and dragging object results. U.S. Patent No. 7,664,739. Washington, DC: U.S. Patent and Trademark Office.
Fawcett, T. and Provost, F. (1997). Adaptive fraud detection. Data Mining and Knowledge Discovery, 1(3), 291–316.Google Scholar
Fayyad, U.M., Wierse, A., and Grinstein, G.G. (Eds.). (2002). Information Visualization in Data Mining and Knowledge Discovery. San Francisco, CA: Morgan Kaufmann.
Federal Trade Commission. (2011). Facebook settles FTC charges that it deceived consumers by failing to keep privacy promises. Federal Trade Commission. Np, 11, 29.
Federal Trade Commission. (2012). Google Will Pay $22.5 million to settle FTC charges it misrepresented privacy assurances to users of apple's safari internet browser. Federal Trade Commission. Np, 8, 9.
Feild, H., White, R.W., and Fu, X. (2013). Supporting orientation during search result examination. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2999–3008). ACM Press.
Feiner, S., Macintyre, B., and Seligmann, D. (1993). Knowledge-based augmented reality. Communications of the ACM, 36(7), 53–62.Google Scholar
Feiner, S.K. (April 2002). Augmented reality: A new way of seeing. Scientific American, 48–55.
Fels, S.S. and Hinton, G.E. (1993). Glove-talk: A neural network interface between a data-glove and a speech synthesizer. IEEE Transactions on Neural Networks, 4(1), 2–8.Google Scholar
Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., and Welty, C. (2010). Building Watson: An overview of the DeepQA project. AI magazine, 31(3), 59–79.Google Scholar
Fertig, S., Freeman, E., and Gelernter, D. (1996). Lifestreams: An alternative to the desktop metaphor. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 410–411).
Fidel, R., Bruce, H., Pejtersen, A.M., Dumais, S.T., Grudin, J., and Poltrock, S. (2000). Collaborative information retrieval (CIR). The New Review of Information Behaviour Research, 1 (January), 235–247.Google Scholar
Fidel, R., Pejtersen, A.M., Cleal, B., and Bruce, H. (2004). A multidimensional approach to the study of human-information interaction: A case study of collaborative information retrieval. Journal of the American Society for Information Science and Technology, 55(11), 939–953.Google Scholar
File, T. and Ryan, C. (2013). Computer and Internet Use in the United States. https://www.census.gov/history/pdf/acs-internet2013.pdf. Accessed on August 17, 2015.
Fisher, D., Popov, I., and Drucker, S. (2012). Trust me, I'm partially right: Incremental visualization lets analysts explore large datasets faster. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1673–1682). ACM Press.
Fisher, R. (1925). Statistical Methods for Research Workers, Edinburgh: Oliver and Boyd, p. 43.
Fishkin, K. and Stone, M.C. (1995). Enhanced dynamic queries via movable filters. In Proceedings of ACM SIGCHI conference on human factors in computing systems (pp. 415–420). ACM Press.
Fiske, S.T., and Hauser, R.M. (2014). Protecting human research participants in the age of big data. Proceedings of the National Academy of Sciences, 111(38), 13675–13676.Google Scholar
Fitts, P.M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381.Google Scholar
Flanagan, J.C. (1954). The critical incident technique. Psychological Bulletin, 51(4), 327.Google Scholar
Florance, V. and Marchionini, G. (1995). Information processing in the context of medical care. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 158–163). ACM Press.
Fogarty, J., Tan, D., Kapoor, A., and Winder, S. (2008). CueFlik: Interactive concept learning in image search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 29–38). ACM Press.
Fogg, B.J. (2002). Motivating, influencing, and persuading users. In Jacko, J. and Sears, A. (Eds.) The Human-Computer Interaction Handbook (pp. 358–370). Hillsdale, NJ: Lawrence Erlbaum Associates Inc.
Foley, J. (2007). Are google searches private: an originalist interpretation of the fourth amendment in online communication cases. Berkeley Technology Law Journal, 22, 447.Google Scholar
Foltz, P. and Landauer, T. (2007). Helping people find and learn from documents: Exploiting synergies between human and computer retrieval with SuperManual. In Landauer, T.K., McNamara, D.S., Dennis, S., and Kintsch, W. (Eds.), The Handbook of Latent Semantic Analysis, pp. 323–345. Mahwah, NJ: Erlbaum.
Ford, N. (1980). Levels of understanding and the personal acceptance of information in higher education. Studies in higher education, 5(1), 63–70.Google Scholar
Ford, N. (1999). Information retrieval and creativity: Towards support for the original thinker. Journal of Documentation, 55(5), 528–542.Google Scholar
Ford, N., Miller, D., and Moss, N. (2005). Web search strategies and human individual differences. Journal of the American Society for Information Science and Technology, 56(7), 741–756.Google Scholar
Foster, A. (2004). A nonlinear model of information-seeking behavior. Journal of the American Society for Information Science and Technology, 55(3), 228–237.Google Scholar
Foster, A. and Ford, N. (2003). Serendipity and information seeking: An empirical study. Journal of Documentation, 59(3), 321–340.Google Scholar
Fourney, A., Mann, R., and Terry, M. (2011). Characterizing the usability of interactive applications through query log analysis. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1817–1826). ACM Press.
Fourney, A. and Morris, M.R. (2013). Enhancing technical Q&A forums with CiteHistory. In Proceedings of the international conference n weblogs and social media. AAAI Press.
Fourney, A., Lafreniere, B., Chilana, P., and Terry, M. (2014). InterTwine: Creating interapplication information scent to support coordinated use of software. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 429–438). ACM Press.
Fourney, A., White, R.W., and Horvitz, E. (2015). Exploring time-dependent concerns about pregnancy and childbirth from search logs. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 737–746). ACM Press.
Fox, S. and Duggan, M. (2013). Online Health 2013. Pew Internet and American Life Project. http://www.pewinternet.org/2013/01/15/health-online-2013. Accessed August 16, 2015.
Fox, S., Karnawat, K., Mydland, M., Dumais, S., and White, T. (2005). Evaluating implicit measures to improve web search. ACM Transactions on Information Systems, 23(2), 147–168.Google Scholar
Franzen, K. and Karlgren, J. (2000). Verbosity and interface design. SICS Research Report.
Freire, A., Macdonald, C., Tonellotto, N., Ounis, I., and Cacheda, F. (2014). A self-adapting latency/power tradeoff model for replicated search engines. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 13–22). ACM Press.CrossRef
Freund, L., Toms, E.G., and Clarke, C.L. (2005). Modeling task-genre relationships for IR in the workplace. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 441–448). ACM Press.
Freyne, J., Farzan, R., Brusilovsky, P., Smyth, B., and Coyle, M. (2007). Collecting community wisdom: Integrating social search and social navigation. In Proceedings of the international conference on intelligent user interfaces (pp. 52–61). ACM Press.
Friedman, J.H. (1999). Stochastic Gradient Boosting. Technical report, Stanford University.
Friggeri, A., Adamic, L.A., Eckles, D., and Cheng, J. (2014). Rumor cascades. In Proceedings of the international AAAI conference on weblogs and social media.
Froehlich, J., Findlater, L., and Landay, J. (2010). The design of eco-feedback technology. In Proceedings of the ACM SIGCHI Conference on human factors in computing systems (pp. 1999–2008). ACM Press.
Fu, X. (2010). Towards a model of implicit feedback for Web search. Journal of the American Society for Information Science and Technology, 61(1), 30–49.Google Scholar
Fu, W.-T. and Pirolli, P. (2007). SNIF-ACT: A cognitive model of user navigation on the world wide web. Human-Computer Interaction, 22(4), 355–412.Google Scholar
Fu, X., Budzik, J., and Hammond, K.J. (2000). Mining navigation history for recommendation. In Proceedings of the international conference on intelligent user interfaces (pp. 106–112). ACM Press.
Fuhr, N. (1989). Optimum polynomial retrieval functions based on the probability ranking principle. ACM Transactions on Information Systems, 7(3), 183–204.Google Scholar
Fulgoni, G.M. (2005). The “Professional Respondent” Problem in Online Survey Panels Today. Slides online at: http://www.sigmavalidation.com/tips/05_06_02_Online_Survey_Panels.ppt. Accessed on August 17, 2015.
Fulton, C. (2009). The pleasure principle: The power of positive affect in information seeking. Aslib Proceedings: New Information Perspectives, 61(3), 245–261.Google Scholar
Funes Mora, K.A., and Odobez, J. (2012). Gaze estimation from multimodal Kinect data. In Computer vision and pattern recognition workshops (pp. 25–30). IEEE.
Furnas, G.W. (1986). Generalized fisheye views. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 16–23). ACM Press.
Furnas, G. (2002). On recommending. Journal of the American Society of Information Science and Technology, 53 (9), 747–763.Google Scholar
Furnas, G.W. (1985). Experience with an adaptive indexing scheme. ACM SIGCHI Bulletin, 16(4), 131–135. ACM Press.Google Scholar
Furnas, G.W., Landauer, T.K., Gomez, L.M., and Dumais, S.T. (1987). The vocabulary problem in human-system communication. Communications of the ACM, 30(11), 964–971.Google Scholar
Gao, J., Yuan, W., Li, X., Deng, K., and Nie, J.Y. (2009). Smoothing clickthrough data for web search ranking. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 355–362). ACM Press.
Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., Bian, K., and Li, X. (2014). Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In Proceedings of the international conference on mobile computing and networking (pp. 249–260). ACM Press.
Gara, T. (2014). My Life, and Past, as Seen Through Google's Dashboard. http://www.wsj.com/articles/SB10001424127887324170004578638402779534498. Accessed on August 15, 2015.
Garfield, E. (1970). When is a negative search result positive?Essays of an Information Scientist, 1, 117–118.Google Scholar
Gauch, S., Chaffee, J., and Pretschner, A. (2003). Ontology-based personalized search and browsing. Web Intelligence and Agent Systems, 1(3), 219–234.Google Scholar
Ge, Y., Xiong, H., Tuzhilin, A., Xiao, K., Gruteser, M., and Pazzani, M. (2010). An energy-efficient mobile recommender system. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 899–908). ACM Press.
Gemmell, J., Bell, G., and Lueder, R. (2006). MyLifeBits: A personal database for everything. Communications of the ACM, 49(1), 88–95.Google Scholar
Gemmell, J., Bell, G., Lueder, R., Drucker, S., and Wong, C. (2002). MyLifeBits: Fulfilling the Memex vision. In Proceedings of the ACM conference on multimedia (pp. 235–238). ACM Press.
Ghani, J.A. and Deshpande, S.P. (1994). Task characteristics and the experience of optimal flow in human-computer interaction. The Journal of Psychology, 128(4), 381–391.Google Scholar
Ghosh, A., Roughgarden, T., and Sundararajan, M. (2012). Universally utility-maximizing privacy mechanisms. SIAM Journal on Computing, 41(6), 1673–1693.Google Scholar
Giannopoulos, G., Brefeld, U., Dalamagas, T., and Sellis, T. (2011). Learning to rank user intent. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 195–200). ACM Press.
Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science, 3, 20–281.Google Scholar
Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., and Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.Google Scholar
Gladwell, M. (2008). Outliers: The Story of Success. Hachette UK.
Glowacka, D., Ruotsalo, T., Konuyshkova, K., Kaski, S., and Jacucci, G. (2013). Directing exploratory search: Reinforcement learning from user interactions with keywords. In Proceedings of the international conference on intelligent user interfaces (pp. 117–128). ACM Press.
Goffman, W.A. (1964). Searching procedure for information retrieval. Information Storage and Retrieval, 2, 73–78.Google Scholar
Golbeck, J.A. (2005). Computing and applying trust in web-based social networks. Unpublished doctoral dissertation. Baltimore: University of Maryland at College Park.
Goldberg, J.H., Stimson, M.J., Lewenstein, M., Scott, N., and Wichansky, A.M. (2002). Eye tracking in web search tasks: Design implications. In Proceedings of the symposium on eye tracking research and applications (pp. 51–58). ACM Press.
Goldman, E. (2008). Search engine bias and the demise of search engine utopianism. Web Search, 14(III), 121–133.Google Scholar
Goldstein, F.C. and Levin, H.S. (1987). Disorders of reasoning and problem-solving ability. In Meier, M., Benton, A., and Diller, L. (Eds.), Neuropsychological Rehabilitation (pp. 327–344). London: Taylor and Francis Group.
Golovchinsky, G. (1997a). What the query told the link: The integration of hypertext and information retrieval. In Proceedings of the ACM conference on hypertext (pp. 67–74).
Golovchinsky, G. (1997b). Queries? Links? Is there a difference?. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 407–414). ACM Press.
Golovchinsky, G., Adcock, J., Pickens, J., Qvarfordt, P., and Back, M. (2008). Cerchiamo: A collaborative exploratory search tool. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 8–12). ACM Press.
Golovchinsky, G., Dunnigan, A., and Diriye, A. (2012). Designing a tool for exploratory information seeking. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 1799–1804). ACM Press.
Gomez, L.M., Lochbaum, C.C., and Landauer, T.K. (1990). All the right words: Finding what you want as a function of richness of indexing vocabulary. Journal of the American Society for Information Science, 41(8), 547–559.Google Scholar
González-Ibáñez, R., Shah, C., and White, R.W. (2012). Pseudo-collaboration as a method to perform selective algorithmic mediation in collaborative IR systems. In Proceedings of the American Society for Information Science and Technology, 49(1), 1–4.Google Scholar
González-Ibáñez, R., Shah, C., and White, R.W. (2015). Capturing collabportunities: A method to evaluate collaboration opportunities in information search using pseudocollaboration. Journal of the Association for Information Science and Technology, 66(9), 1897–1912.Google Scholar
Google Webmaster Central Blog. (April 9, 2010). Using site speed in web search ranking. http://bit.ly/acUf3Q.
Gould, J.D. and Lewis, C. (1985). Designing for usability: Key principles and what designers think. Communications of the ACM, 28(3), 300–311.Google Scholar
Granger, C.W.J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.Google Scholar
Granka, L.A., Joachims, T., and Gay, G. (2004). Eye-tracking analysis of user behavior in WWW search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 478–479). ACM Press.
Granovetter, M. (1973). Strength of weak ties. American Journal of Sociology, 78, 1360–1380.Google Scholar
Gray, B. (1989). Collaborating: Finding Common Ground for Multiparty Problems. San Francisco, CA:Jossey-Bass.
Green, T.R. (1991). Describing information artifacts with cognitive dimensions and structure maps. In Human Computer Interaction, 91(748), 297–315.Google Scholar
Greenberg, S. and Cockburn, A. (1999). Getting back to back: Alternate behaviors for a Web browser's back button. In Proceedings of the annual human factors and the Web conference.
Greer, J.E., McCalla, G.I., Cooke, J.E., Collins, J., Kumar, V.S., Bishop, A., and Vassileva, J.I. (1998). The intelligent helpdesk: Supporting peer-help in a university course. In Proceedings of the intelligent tutoring systems conference (pp. 494–505).
Grimes, C., Tang, D., and Russell, D.M. (2007). Query logs alone are not enough. In Proceedings of the query log analysis workshop at the international conference on the World Wide Web.
Grudin, J. (1994). Groupware and social dynamics: Eight challenges for developers. Communications of the ACM, 37(1), 92–105.Google Scholar
Guha, R., McCool, R., and Miller, E. (2003). Semantic search. In Proceedings of the international conference on the World Wide Web (pp. 700–709). ACM Press.
Guilford, J.P. (1967). The nature of human intelligence. New York: McGraw-Hill.
Guimbretière, F. and Nguyen, C. (2012). Bimanual marking menu for near surface interactions. In Proceedings of the ACM SIGCHI annual conference on human factors in computing systems (pp. 825–828). ACM Press.
Guinan, C. and Smeaton, A.F. (1992). Information retrieval from hypertext using dynamically planned guided tours. In Proceedings of the ACM conference on hypertext (pp. 122–130). ACM Press.
Gunduz, S.U. and Özsu, M.T., (2003). Recommendation models for user accesses to web pages. In Proceedings of the conference on artificial neural networks (pp. 1003–1010).
Guo, Q. and Agichtein, E. (2008). Exploring mouse movements for inferring query intent. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 707–708). ACM Press.
Guo, Q., Agichtein, E., Clarke, C.L., and Ashkan, A. (2009). In the mood to click? Towards inferring receptiveness to search advertising. In Proceedings of the international joint conference on Web intelligence and intelligent agent technology (pp. 319–324). IEEE Computer Society.
Guo, Q. and Agichtein, E. (2010). Ready to buy or just browsing? Detecting web searcher goals from interaction data. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 130–137). ACM Press.
Guo, Q., White, R.W., Dumais, S.T., Wang, J., and Anderson, B. (2010). Predicting query performance using query, result, and user interaction features. In Proceedings of adaptivity, personalization and fusion of heterogeneous information (pp. 198–201).
Guo, Q. and Agichtein, E. (2012). Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior. In Proceedings of the international conference on the World Wide Web (pp. 569–578). ACM Press.
Guo, Q., Diaz, F., and Yom-Tov, E. (2013). Updating users about time critical events. In Proceedings of the European conference on information retrieval (pp. 483–494). Springer-Verlag.
Guo, Q., Yuan, S., and Agichtein, E. (2011). Detecting success in mobile search from interaction. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1229–1230). ACM Press.
Guo, Q., Jin, H., Lagun, D., Yuan, S., and Agichtein, E. (2013). Mining touch interaction data on mobile devices to predict web search result relevance. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 153–162). ACM Press.
Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157–1182.Google Scholar
Gwizdka, J. (2008). Revisiting search task difficulty: Behavioral and individual difference measures. Proceedings of the American Society for Information Science and Technology, 45(1), 1–12.Google Scholar
Gwizdka, J. (2010). Distribution of cognitive load in web search. Journal of the American Society for Information Science and Technology, 61(11), 2167–2187.Google Scholar
Gwizdka, J. and Spence, I. (2006). What can searching behavior tell us about the difficulty of information tasks? A study of web navigation. In Proceedings of the American society for information science and technology, 43(1), 1–22.Google Scholar
Gwizdka, J. and Spence, I. (2007). Implicit measures of lostness and success in web navigation. Interacting with Computers, 19(3), 357–369.Google Scholar
Gwizdka, J. and Zhang, Y. (2015). Differences in eye-tracking measures between visits and revisits to relevant and irrelevant web pages. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 811–814). ACM Press.
Hammond, N. and Allison, L. (1988). Travels around a learning support environment: Rambling, orienteering, or touring? In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 269–273). ACM Press.
Han, J., Kamber, M., and Pei, J. (2006). Data Mining: Concepts and Techniques. San Francisco, CA: Morgan Kaufmann.
Han, S., Yue, Z., and He, D. (2013). Automatic detection of search tactic in individual information seeking: A hidden markov model approach. In Proceedings of the iConference (pp. 712–716).
Hancock-Beaulieu, M. and Walker, S. (1992). An evaluation of automatic query expansion in an online library catalog. Journal of Documentation, 48, 406–421.Google Scholar
Hansen, P. and Järvelin, K. (2000). The information seeking and retrieval process at the Swedish patent and registration office. In Proceedings of the ACM SIGIR Workshop on Patent Retrieval.
Harding, J. (2013). Qualitative Data Analysis from Start to Finish. London, SAGE Publishers.
Harel, M.G.O. and Yom-Tov, E. (2015). Modularity-based query clustering for identifying users sharing a common condition. In Proceedings the ACM SIGIR conference on research and development in information retrieval (pp. 819–822). ACM Press.
Harman, D. (1988). Towards interactive query expansion. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 321–331).
Harman, D. (1993). Overview of the first TREC conference. In Proceedings of the ACM SIGIR conference of research and development in information retrieval (pp. 36–47).
Harman, D.K. (1997). The TREC conferences. In Jones, K.S. (Ed.), Readings in Information Retrieval (pp. 247–256). San Francisco, CA: Morgan Kaufmann Publishers Inc.
Harman, D. (2011). Information retrieval evaluation. Synthesis Lectures on Information Concepts, Retrieval, and Services, 3(2), 1–119.Google Scholar
Hart, W., Albarracín, D., Eagly, A.H., Brechan, I., Lindberg, M.J., and Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological bulletin, 135(4), 555.Google Scholar
Haro, A., Essa, I., and Flickner, M. (2000). A non-invasive computer vision system for reliable eye tracking. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 167–168). ACM Press.
Harpaz, R., Odgers, D., Gaskin, G., DuMouchel, W., Winnenburg, R., Bodenreider, O., Ripple, A., Szarfman, A., Sorbello, A., Horvitz, E., White, R.W., and Shah, N. (2014). A time-indexed reference standard of adverse drug reactions. Nature Scientific Data, 1.Google Scholar
Harper, D.J., Coulthard, S., and Yixing, S. (2002). A language modelling approach to relevance profiling for document browsing. In Proceedings of the joint conference on digital libraries (pp. 76–83). ACM Press.
Harper, D.J., Koychev, I., Sun, Y. and Pirie, I. (2004). Within-document retrieval: A user-centred evaluation of relevance profiling. Information Retrieval, 7(3–4), 265–290.Google Scholar
Harper, F.M., Raban, D., Rafaeli, S., and Konstan, J.A. 2008. Predictors of answer quality in online QandA sites. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 865–874). ACM Press.
Harriman, S. and Patel, J. (2014). The ethics and editorial challenges of internet-based research. BMC Medicine, 12(1), 124.Google Scholar
Harrison, C., Tan, D., and Morris, D. (2010). Skinput: Appropriating the body as an input surface. Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 453–462). ACM Press.
Harrison, W. (2006). Eating your own dog food. IEEE Software, 23(3), 5–7.Google Scholar
Hart, S.G. and Staveland, L.E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in psychology, 52, 139–183.Google Scholar
Harter, S.P. (1992). Psychological relevance and information science. Journal of the American Society for Information Science, 43(9), 602.Google Scholar
Hassan, A. (2012). A semi-supervised approach to modeling web search satisfaction. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 275–284). ACM Press.
Hassan, A. and White, R.W. (2012). Task tours: Helping users tackle complex search tasks. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1885–1889). ACM Press.
Hassan, A., Jones, R., and Klinkner, K.L. (2010). Beyond DCG: User behavior as a predictor of a successful search. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 221–230). ACM Press.
Hassan, A., Song, Y., and He, L.W. (2011). A task level metric for measuring web search satisfaction and its application on improving relevance estimation. In Proceedings of the ACM conference on information and knowledge management (pp. 125–134). ACM Press.
Hassan, A. and White, R.W. (2013). Personalized models of search satisfaction. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 2009–2018). ACM Press.
Hassan, A., White, R.W., and Wang, Y.M. (2013). Toward self-correcting search engines: Using underperforming queries to improve search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 263–272). ACM Press.
Hassan, A., White, R.W., Dumais, S.T., and Wang, Y.M. (2014). Struggling or exploring?: Disambiguating long search sessions. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 53–62). ACM Press.
Hassan Awadallah, A., White, R.W., Pantel, P., Dumais, S.T., and Wang, Y.M. (2014). Supporting complex search tasks. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 829–838). ACM Press.
Hassan Awadallah, A., Kulkarni, R.G., Ozertem, U. and Jones, R. (2015). Characterizing and predicting voice query reformulation. In Proceedings of the ACM CIKM conference on information and knowledge management (in press). ACM Press.
Hauff, C., Kelly, D., and Azzopardi, L. (2010). A comparison of user and system query performance predictions. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 979–988). ACM Press.
Hauff, C., Hiemstra, D., and de Jong, F. (2008). A survey of pre-retrieval query performance predictors. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1419–1420). ACM Press.
He, B. and Ounis, I. (2004). Inferring query performance using pre-retrieval predictors. In String Processing and Information Retrieval (pp. 43–54). SpringerBerlin Heidelberg.
He, D., Göker, A., and Harper, D.J. (2002). Combining evidence for automatic web session identification. Information Processing and Management, 38(5), 727–742.Google Scholar
He, J., Larson, M., and De Rijke, M. (2008). Using coherence-based measures to predict query difficulty. In Advances in Information Retrieval (pp. 689–694). SpringerBerlin Heidelberg.
He, Q., Kifer, D., Pei, J., Mitra, P., and Giles, C.L. (2011). Citation recommendation without author supervision. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 755–764). ACM Press.
Healey, J. and Picard, R.W. (1998). Startlecam: a cybernetic wearable camera. In Proceedings of the international symposium on wearable computers (pp. 42–49). IEEE.
Hearst, M. (2009). Search User Interfaces. Cambridge: Cambridge University Press.
Hearst, M., Elliott, A., English, J., Sinha, R., Swearingen, K., and Yee, K.P. (2002). Finding the flow in web site search. Communications of the ACM, 45(9), 42–49.Google Scholar
Hearst, M. A. (1995). TileBars: Visualization of term distribution information in full text information access. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 59–66). ACM Press/Addison-Wesley Publishing Co.
Hearst, M.A. (1997). TextTiling: Segmenting text into multi-paragraph subtopic passages. Computational linguistics, 23(1), 33–64.Google Scholar
Hearst, M.A. (1999). User interfaces and visualization. In Baeza-Yates, R. and Ribeiro-Neto, B. (eds.), Modern Information Retrieval (pp. 257–323). Reading, MA: Addison Wesley Longman.
Hearst, M.A. (2000). Next generation web search: Setting our sites. IEEE Data Engineering Bulletin, 23(3), 38–48.Google Scholar
Hearst, M.A. (2006). Clustering versus faceted categories for information exploration. Communications of the ACM, 49(4), pp. 59–61.Google Scholar
Hearst, M.A. (2011). “Natural” search user interfaces. Communications of the ACM, 54(11), 60–67.Google Scholar
Hearst, M.A. (2014a). What's missing from collaborative search?IEEE Computer, 3, 58–61.Google Scholar
Hearst, M. A. (2014b). Seeking simplicity in search user interfaces. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 333–334). ACM Press.
Hearst, M.A. and Karadi, C. (1997). Cat-a-Cone: An interactive interface for specifying searches and viewing retrieval results using a large category hierarchy. ACM SIGIR Forum, 31(SI), 246–255. ACM Press.CrossRef
Hearst, M.A. and Plaunt, C. (1993). Subtopic structuring for full-length document access. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 59–68). ACM Press.
Heath, A.P. and White, R.W. (2008). Defection detection: Predicting search engine switching. In Proceedings of the international conference on the World Wide Web (pp. 1173–1174). ACM Press.
Hecht, B. and Stephens, M. (2014). A tale of cities: Urban biases in volunteered geographic information. In Proceedings of the international conference on weblogs and social media. AAAI Press.
Hecht, B., Teevan, J., Morris, M.R., and Liebling, D.J. (2012). SearchBuddies: Bringing search engines into the conversation. In Proceedings of the international conference on weblogs and social media (pp. 138–145). AAAI Press.
Heckerman, D.E., Horvitz, E.J., and Nathwani, B.N. (1989). The Pathfinder system. In Proceedings of the annual symposium on computer application [sic] in medical care (pp. 203–207). American Medical Informatics Association.
Heer, J. and Chi, E.H. (2001). Identification of web user traffic composition using multi-modal clustering and information scent. In Proceedings of the workshop on web mining, siam conference on data mining (pp. 51–58).
Heitz, G. and Koller, D. (2008). Learning spatial context: Using stuff to find things. In Computer Vision–ECCV 2008 (pp. 30–43). Springer Berlin Heidelberg.
Hellerstein, J.M., Avnur, R., Chou, A., Hidber, C., Olston, C., Raman, V., and Haas, P.J. (1999). Interactive data analysis: The control project. Computer, 32(8), 51–59.Google Scholar
Hendahewa, C. and Shah, C. (2015). Implicit search feature based approach to assist users in exploratory search tasks. Information Processing and Management, 51(5), 643–661.Google Scholar
Hendry, D.G. and Harper, D.J. (1997). An informal information-seeking environment. Journal of the American Society for Information Science, 48(11), 1036–1048.Google Scholar
Henrion, M., Breese, J.S., and Horvitz, E.J. (1991). Decision analysis and expert systems. Artificial Intelligence Magazine, 12(4), 64.Google Scholar
Henze, N., Rukzio, E., and Boll, S. (2012). Observational and experimental investigation of typing behaviour using virtual keyboards for mobile devices. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2659–2668). ACM Press.
Henzinger, M.R. (2001). Hyperlink analysis for the Web. Internet Computing, 5(1), 45–50.Google Scholar
Herder, E. (2005). Characterizations of user Web revisit behavior. In Proceedings of workshop on adaptivity and user modeling in interactive systems.
Herlocker, J.L., Konstan, J.A., and Riedl, J. (2000). Explaining collaborative filtering recommendations. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 241–250). ACM Press.
Hersh, W., Turpin, A., Price, S., Chan, B., Kramer, D., Sacherek, L., and Olson, D. (2000). Do batch and user evaluations give the same results? In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 17–24). ACM Press.
Hess, E.H., and Polt, J.M. (1960). Pupil size as related to interest value of stimuli. Science, 132(3423), 349–350.Google Scholar
Hilbert, M. and López, P. (2011). The world's technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.Google Scholar
Hill, J.R. and Hannafin, M.J. (1997). Cognitive strategies and learning from the World Wide Web. Educational Technology Research and Development, 45(4), 37–64.Google Scholar
Hill, J.R. (1999). A conceptual framework for understanding information seeking in open-ended information systems. Educational Technology Research and Development, 47(1), 5–27.Google Scholar
Hill, W.C., Hollan, J.D., Wroblewski, D., and McCandless, T. (1992). Edit wear and read wear. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 3–9). ACM Press.
Hill, W.C. and Hollan, J.D. (1994). History-enriched digital objects: Prototypes and policy issues. The Information Society, 10(2), 139–145.Google Scholar
Hill, W.C., Hollan, J.D., Wroblewski, D., and McCandless, T. (1992). Edit wear and read wear. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 3–9). ACM Press.
Himmelstein, M. (2005). Local search: The internet is the yellow pages. IEEE Computer, 38(2), 26–34.Google Scholar
Hinkelmann, K., and Kempthorne, O. (1994). Design and Analysis of Experiments: Volume 1: Introduction to Experimental Design. Hoboken, NJ: John Wiley and Sons.
Hinckley, K., Zhao, S., Sarin, R., Baudisch, P., Cutrell, E., Shilman, M., and Tan, D. (2007). InkSeine: In situ search for active note taking. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 251–260). ACM Press.
Hinton, G.E. and Salakhutdinov, R.R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.Google Scholar
Hirshfield, L.M., Chauncey, K., Gulotta, R., Girouard, A., Solovey, E.T., Jacob, R.J.K., Sassaroli, A., and Fantini, S. (2009). Combining electroencephalograph and functional near infrared spectroscopy to explore users’ mental workload. In Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience (pp. 239–47). Springer Berlin / Heidelberg.
Hirsh-Pasek, K., Golinkoff, R.M., Berk, L.E., and Singer, D. (2008). A Mandate for Playful Learning in Preschool: Applying the Scientific Evidence. Oxford: Oxford University Press.
Hoellerer, T., Feiner, S., Terauchi, T., Rashid, G.,and Hallaway, D. (1999). Exploring mars: Developing indoor and outdoor user interfaces to a mobile augmented reality system. Computers and Graphics, 23(6), 779–785.Google Scholar
Höller, J., Tsiatsis, V., Mulligan, C., Karnouskos, S., Avesand, S., and Boyle, D. (2014). From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence. Elsevier.
Hölscher, C. and Strube, G. (2000). Web search behavior of Inter-net experts and newbies. Computer Networks, 33, 337–346.Google Scholar
Holz, C. and Wilson, A. (2011). Data miming: inferring spatial object descriptions from human gesture. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 811–820). ACM Press.
Hong, J. (2013). Considering privacy issues in the context of Google glass. Communications of the ACM, 56(11), 10–11.Google Scholar
Horowitz, A., Jacobson, D., McNichol, T., and Thomas, O. (2007). 101 dumbest moments in business, the year's biggest boors, buffoons, and blunderers. CNN Money. http://money.cnn.com/galleries/2007/biz2/0701/gallery.101dumbest_2007/index.html.
Horowitz, D., and Kamvar, S.D. (2010). The anatomy of a large-scale social search engine. In Proceedings of the international conference on World Wide Web (pp. 431–440). ACM.
Horvitz, E. (1997). Models of continual computation. In Proceedings of the AAAI conference on artificial intelligence (pp. 286–293). AAAI Press.
Horvitz, E. (1998). Continual computation policies for utility-directed prefetching. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 175–184). ACM Press.
Horvitz, E. (1999). Principles of mixed-initiative user interfaces. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 159–166). ACM Press.
Horvitz, E. (2001). Principles and applications of continual computation. Artificial Intelligence, 126(1), 159–196.Google Scholar
Horvitz, E. and Apacible, J. (2003). Learning and reasoning about interruption. In Proceedings of the international conference on multimodal interfaces (pp. 20–27). ACM Press.
Horvitz, E. and Barry, M. (1995). Display of information for time-critical decision making. In Proceedings of the conference on uncertainty in artificial intelligence (pp. 296–305). Morgan Kaufmann Publishers Inc.
Horvitz, E., Dumais, S., and Koch, P. (2004). Learning predictive models of memory landmarks. In Proceedings of the annual meeting of the cognitive science society (pp. 583–588). Lawrence Erlbaum Associates.
Horvitz, E., Jacobs, A., and Hovel, D. (1999). Attention-sensitive alerting. In Proceedings of the conference on uncertainty in artificial intelligence (pp. 305–313). Morgan Kaufmann Publishers Inc.
Horvitz, E. and Mulligan, D. (2015). Data, privacy, and the greater good. Science, 349(6245), 253–255.
Horvitz, E. and Rutledge, G. (1991). Time-dependent utility and action under uncertainty. In Proceedings of the conference on uncertainty in artificial intelligence (pp. 151–158). Morgan Kaufmann Publishers Inc.
Horvitz, E. and Seiver, A. (1997). Time-critical action: Representations and application. In Proceedings of the conference on uncertainty in artificial intelligence (pp. 250–257). Morgan Kaufmann Publishers Inc.
Horvitz, E. and Shwe, M. (1995). In pursuit of effective handsfree decision support: Coupling bayesian inference. In Proceedings of the anuual symposium on computer applications in medical care. toward cost-effective clinical computing.
Howe, D. and Nissenbaum, H. (2008). TrackMeNot: Resisting surveillance in web search. In On the Identity Trail: Privacy, Anonymity and Identity in a Networked Society. Oxford: Oxford University Press.
Hoyle, R., Templeman, R., Armes, S., Anthony, D., Crandall, D., and Kapadia, A. (2014). Privacy behaviors of lifeloggers using wearable cameras. In Proceedings of the ACM international joint conference on pervasive and ubiquitous computing (pp. 571–582). ACM Press.
Hsieh, G. and Counts, S. (2009). Mimir: A market-based real-time question and answer service. In Proceedings of the ACM SIGCHI on human factors in computing systems (pp. 769–778). ACM Press.
Hsieh-Yee, I. (1993). Effects of search experience and subject knowledge on the search tactics of novice and experience users. Journal of the American Society for Information Science, 44(3), 161–174.Google Scholar
Hu, V., Stone, M., Pedersen, J., and White, R.W. (2011). Effects of search success on search engine re-use. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1841–1846). ACM Press.
Huang, J. and Diriye, A. (2012). Web user interaction mining from touch-enabled mobile devices. In Proceedings of the symposium on human-computer interaction and retrieval.
Huang, J. and Efthimiadis, E.N. (2009). Analyzing and evaluating query reformulation strategies in web search logs. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 77–86). ACM Press.
Huang, J. and White, R.W. (2010). Parallel browsing behavior on the web. In Proceedings of the ACM conference on hypertext and hypermedia (pp. 13–18). ACM Press.
Huang, J., White, R.W., and Buscher, G. (2012). User see, user point: Gaze and cursor alignment in web search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1341–1350). ACM Press.
Huang, J., White, R.W., and Dumais, S.T. (2011). No clicks, no problem: Using cursor movements to understand and improve search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1225–1234). ACM Press.
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., and Heck, L. (2013). Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 2333–2338). ACM Press.
Huberman, B.A., Pirolli, P.L., Pitkow, J.E., and Lukose, R.M. (1998). Strong regularities in world wide web surfing. Science, 280(5360), 95–97.Google Scholar
Huffman, S.B. and Hochster, M. (2007). How well does result relevance predict session satisfaction?. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 567–574). ACM Press.
Hughes, R.N. (1997). Intrinsic exploration in animals: Motives and measurement. Behavioural Processes, 41(3), 213–226.Google Scholar
Huvila, I. and Widén-Wulff, G. (2006). Perspectives to the classification of information interactions: the Cool and Belkin faceted classification scheme under scrutiny. In Proceedings of the IIiX conference on information interaction in context (pp. 144–152). ACM Press.
Hunt, R.R. (1995). The subtlety of distinctiveness: What von Restorff really did. Psychonomic Bulletin and Review, 2, 105–112.Google Scholar
Hutchinson, T.E., WhiteJr, K.P., Martin, W.N., Reichert, K.C., and Frey, L.A. (1989). Human-computer interaction using eye-gaze input. IEEE Transactions on Systems, Man and Cybernetics, 19(6), 1527–1534.Google Scholar
Hyldegård, J. (2006). Collaborative information behaviour: Exploring Kuhlthau's Information Search Process model in a group-based educational setting. Information Processing and Management, 42(1), 276–298.Google Scholar
Hyldegård, J. (2009). Beyond the search process: Exploring group members’ information behavior in context. Information Processing and Management, 45(1), 142–158.Google Scholar
IBM (2013). What is big data? – Bringing big data to the enterprise. http://www-01.ibm.com/software/in/data/bigdata. Accessed on August 26, 2013.
Inagaki, Y., Sadagopan, N., Dupret, G., Dong, A., Liao, C., Chang, Y., and Zheng, Z. (2010). Session based click features for recency ranking. In Proceedings of the AAAI conference on artificial intelligence (pp. 1334–1339). AAAI Press.
Ieong, S., Mishra, N., Sadikov, E., and Zhang, L. (2012). Domain bias in Web search. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 413–422). ACM Press.
Ingwersen, P. (1982). Search procedures in the library analysed from the cognitive point of view. Journal of Documentation, 38, 165–191.Google Scholar
Ingwersen, P. (1984). A cognitive view of three selected online search facilities. Online Information Review, 8(5), 465–492.Google Scholar
Ingwersen, P. and Pejtersen, A.M. (1986). User requirements – empirical research and information systems design. In Information technology and information use: Towards a unified view of information and information technology (pp. 111–124). Taylor Graham Publishing.
Ingwersen, P. (1992). Information Retrieval Interaction. London: Taylor Graham.
Ingwersen, P. (1994). Polyrepresentation of information needs and semantic entities elements of a cognitive theory for information retrieval interaction. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 101–110). SpringerLondon.
Ingwersen, P. (1996). Cognitive perspectives of information retrieval interaction: Elements of a cognitive IR theory. Journal of Documentation, 52(1), 3–50.Google Scholar
Ingwersen, P. (2002). Cognitive perspectives of document representation. In Proceedings of the COLIS conference on conceptions of library and information science (pp. 285–300).
Ingwersen, P. and Järvelin, K. (2005). The Turn: Integration of Information Seeking and Retrieval in Context. New York: Springer-Verlag.
Intons-Peterson, M.J. and Fournier, J. (1986). External and internal memory aids: When and how often do we use them?. Journal of Experimental Psychology: General, 115(3), 267.Google Scholar
Iqbal, S.T. and Horvitz, E. (2007). Disruption and recovery of computing tasks: field study, analysis, and directions. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 677–686). ACM Press.
Iqbal, S.T., Zheng, X.S., and Bailey, B.P. (2004). Task-evoked pupillary response to mental workload in human-computer interaction. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 1477–1480). ACM Press.
Isaacs, E., Walendowski, A., Whittaker, S., Schiano, D.J., and Kamm, C. (2002). The character, functions, and styles of instant messaging in the workplace. In Proceedings of the ACM CSCW computer supported cooperative work (pp. 11–20). ACM Press.
Issacs, E.A. and Clark, H. (1987). References in conversations between experts and novices. Journal of Experimental Psychology, 116(1), 26–37.Google Scholar
Ives, B., Olson, M.H., and Baroudi, J.J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785–793.Google Scholar
Iwayama, M. (2000). Relevance feedback with a small number of relevance judgements: Incremental relevance feedback vs. document clustering. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 10–16). ACM Press.
Jacob, R.J. (1990). What you look at is what you get: Eye movement-based interaction techniques. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 11–18). ACM Press.
Jacques, R.D. (1996). The nature of engagement and its role in hypermedia evaluation and design. Unpublished doctoral dissertation, South Bank University, London.
Jaggi, M. and Sulovsk, M. (2010). A simple algorithm for nuclear norm regularized problems. In Proceedings of the international conference on machine learning (pp. 471–478).
Janes, J.W. (1991). Relevance judgements and the incremental presentation of document representations. Information Processing and Management, 27(6), 629–646.Google Scholar
Jansen, B.J. (2006a). Search log analysis: What it is, what's been done, how to do it. Library and Information Science Research, 28(3), 407–432.Google Scholar
Jansen, B.J. (2006b). The Wrapper: An open source application for logging user-system interactions during search studies. In Proceedings of the workshop on logging traces of web activity at the World Wide Web conference.
Jansen, B.J., Booth, D., and Smith, B. (2009). Using the taxonomy of cognitive learning to model online searching. Information Processing and Management, 45(6), 643–663.CrossRefGoogle Scholar
Jansen, B.J. and Spink, A. (2005). How are we searching the World Wide Web? A comparison of nine search engine transaction logs. Information Processing and Management, 42(1), 248–263.Google Scholar
Jansen, B.J., Spink, A., Blakely, C., and Koshman, S. (2007). Defining a session on Web search engines. Journal of the American Society for Information Science and Technology, 58(6), 862–871.Google Scholar
Jansen, B.J., Spink, A., and Saracevic, T. (2000). Real life, real users, and real needs: A study and analysis of user queries on the web. Information Processing and Management, 36 (2), 207–227.Google Scholar
Jardine, N. and Van Rijsbergen, C.J. (1971). The use of hierarchic clustering in information retrieval. Information storage and retrieval, 7(5), 217–240.CrossRefGoogle Scholar
Järvelin, K. and Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4), 422–446.Google Scholar
Järvelin, K., Price, S.L., Delcambre, L.M., and Nielsen, M.L. (2008). Discounted cumulated gain based evaluation of multiple-query IR sessions. In Proceedings of the European conference on information retrieval (pp. 4–15). Springer Berlin Heidelberg.
Jarvis, P., Holford, J., and Griffin, C. (Eds.). (2003). The Theory and Practice of Learning. London: Routledge.
Jeh, G. and Widom, J. (2003). Scaling personalized web search. In Proceedings of the international conference on the World Wide Web (pp. 271–279). ACM Press.
Jennings, R.B., Nahum, E.M., Olshefski, D.P., Saha, D., Shae, Z.Y., and Waters, C. (2006). A study of internet instant messaging and chat protocols. IEEE, Network, 20(4), 16–21. IEEE.Google Scholar
Jensen, C., Farnham, S.D., Drucker, S.M., and Kollock, P. (2000). The effect of communication modality on cooperation in online environments. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 470–477). ACM Press.CrossRef
Jermaine, C., Aramugam, S., Pol, A., and Dobra, A. (2007). Scalable approximate query processing with the DBO engine. In Proceedings of the ACM SIGMOD conference on the management of data (pp. 725–736). ACM Press.
Jiang, J., Hassan, A., Jones, R., Ozertem, U., Zitouni, I., Kulkarni, R.G., and Khan, O.Z. (2015). Automatic online evaluation of intelligent assistants. In Proceedings of the international conference on the World Wide Web (pp. 506–516). International World Wide Web Conferences Steering Committee.
Jiang, J., Hassan, A., Shi, X., and White, R.W. (2015). Understanding and predicting graded search satisfaction. In Proceedings of the ACM WSDM conference on Web search and data mining. (pp. 57–66). ACM Press.
Jiang, J., He, D., Han, S., Yue, Z., and Ni, C. (2012). Contextual evaluation of query reformulations in a search session by user simulation. In Proceedings of the ACM CIKM international conference on information and knowledge management (pp. 2635–2638). ACM Press.
Joachims, T. (2002a). Evaluating retrieval performance using click through data. In Proceedings of the workshop on mathematical/formal methods in IR (pp. 12–15).
Joachims, T. (2002b). Optimizing search engines using clickthrough data. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 133–142). ACM Press.
Joachims, T., Freitag, D., and Mitchell, T. (1997). WebWatcher: A tour guide for the world wide web. In Proceedings of the joint international conference on artificial intelligence (pp. 770–775).
Joachims, T., Granka, L., Pan, B., Hembrooke, H., and Gay, G. (2005). Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 154–161). ACM Press.
Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., and Gay, G. (2007). Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Transactions on Information Systems, 25(2), 7.Google Scholar
Johnson, E.A. (1965). Touch display: A novel input/output device for computers. Electronics Letters 1(8), 219–220Google Scholar
Johnson, E.A. (1967). Touch displays: A programmed man-machine interface. Ergonomics, 10(2), 271–277.Google Scholar
Johnson, H.A., Wagner, M.M., Hogan, W.R., Chapman, W., Olszewski, R.T., Dowling, J., and Barnas, G. (2004). Analysis of Web access logs for surveillance of influenza. Studies in Health Technology and Informatics, 107(Pt 2), 1202–1206.Google Scholar
Johnson-Laird, P.N. (1983). Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness (No. 6). Cambridge, MA: Harvard University Press.
Jones, B.R., Benko, H., Ofek, E., and Wilson, A.D. (2013). IllumiRoom: Peripheral projected illusions for interactive experiences. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 869–878). ACM Press.
Jones, B., Sodhi, R., Murdock, M., Mehra, R., Benko, H., Wilson, A., and Shapira, L. (2014). RoomAlive: Magical experiences enabled by scalable, adaptive projector-camera units. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 637–644). ACM Press.
Jones, R., Hassan, A., and Diaz, F. (2008). Geographic features in web search retrieval. In Proceedings of the international workshop on geographic information retrieval (pp. 57–58). ACM Press.
Jones, R. and Klinkner, K.L. (2008). Beyond the session timeout: Automatic hierarchical segmentation of search topics in Query logs. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 699–708). ACM Press.
Jones, R., Kumar, R., Pang, B., and Tomkins, A. (2007). I know what you did last summer: Query logs and user privacy. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 909–914). ACM Press.
Jones, R., Rey, B., Madani, O., and Greiner, W. (2006). Generating query substitutions. In Proceedings of the international conference on the World Wide Web (pp. 387–396). ACM Press.
Jones, W. (2007). Personal information management. Annual Review of Information Science and Technology, 41(1), 453–504.Google Scholar
Jones, W., Phuwanartnurak, A. J., Gill, R., and Bruce, H. (2005). Don't take my folders away!: Organizing personal information to get things done. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 1505–1508). ACM Press.
Jøsang, A. (2007). Trust and reputation systems. In Aldini, A. (Ed.), Foundations of Security Analysis and Design IV (pp. 209–245). Springer-Verlag.
Jose, J.M., Furner, J., and Harper, D.J. (1998). Spatial querying for image retrieval: a user-oriented evaluation. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 232–240). ACM Press.
Juan, Y.F. and Chang, C.C. (2005). An analysis of search engine switching behavior using click streams. In Proceedings of the international conference on the World Wide Web (pp. 1050–1051). ACM Press.
Jung, C.G. (1923). Psychological Types. Routledge: London.
Jung, C.G. (1953). Two Essays on Analytical Psychology. Routledge: London.
Junker, H., Lukowitz, P., and Troester, G. (2004). Continuous recognition of arm activities with body-worn inertial sensors. In Proceedings of the International Semantic Web Conference (pp. 188–189). IEEE.
Jurczyk, P. and Agichtein, E. (2007). Discovering authorities in question answer communities by using link analysis. In Proceedings of the ACM CIKM conference on nformation and knowledge management (pp. 919–922). ACM Press.
Kaasten, S. and Greenberg, S. (2000). Designing an integrated bookmark/history system for Web browsing. In Proceedings of the western computer graphics symposium.
Kaki, M. (2005). Findex: Search result categories help users when document ranking fails. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 131–140). ACM Press.
Kammerer, Y., Nairn, R., Pirolli, P., and Chi, E.H. (2009). Signpost from the masses: learning effects in an exploratory social tag search browser. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 625–634). ACM Press.
Kanoulas, E., Carterette, B., Clough, P., and Sanderson, M. (2010). Session track overview. In Proceedings of the text retrieval conference (p. 11).
Kanoulas, E., Carterette, B., Clough, P.D., and Sanderson, M. (2011). Evaluating multi-query sessions. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1053–1062). ACM Press.
Kantor, P. (1988). National, language-specific evaluation sites for retrieval systems and interfaces. In Proceedings of the RIAO conference on computer-assisted information retrieval (pp. 139–147).
Kantor, P., Bores, E., Melamed, B., Neu, D., Menkov, V., and Kim, M.H. (1999). Ant World. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (p. 323). ACM Press.
Kantor, P.B., Boros, E., Melamed, B., Meñkov, V., Shapira, B., and Neu, D.J. (2000). Capturing human intelligence in the net. Communications of the ACM, 43(8), 112–115.Google Scholar
Kaplan, K. (2014). Telecommuting: No place like home. Nature, 506(7486), 121–123.Google Scholar
Kapoor, A., Burleson, W., and Picard, R.W. (2007). Automatic prediction of frustration. International Journal of Human-Computer Studies, 65(8), 724–736.Google Scholar
Kapoor, A., Lee, B., Tan, D., and Horvitz, E. (2010). Interactive optimization for steering machine classification. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1343–1352). ACM Press.
Kapoor, A., Mota, S., and Picard, R.W. (2001). Towards a learning companion that recognizes affect. In Proceedings of the AAAI fall symposium (pp. 2–4).
Kapoor, A. and Picard, R.W. (2005). Multimodal affect recognition in learning environments. In Proceedings of the ACM international conference on multimedia (pp. 677–682). ACM Press.
Kaptelinin, V. and Nardi, B. (2006). Acting with Technology: Activity Theory and Interaction Design. Cambridge: MIT Press.
Kari, J. and Hartel, J. (2007). Information and higher things in life: Addressing the pleasurable and the profound in information science. Journal of the American Society for Information Science and Technology, 58(8), 1131–1147.Google Scholar
Karimzadehgan, M., White, R.W., and Richardson, M. (2009). Enhancing expert finding using organizational hierarchies. In Proceedings of the European conference on information retrieval (pp. 177–188). Springer Berlin Heidelberg.
Karlson, A.K., Iqbal, S.T, Meyers, B., Ramos, G., Lee, K., and Tang, J.C. (2010). Mobile taskflow in context: A screenshot study of smartphone usage. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2009–2018). ACM Press.
Kashdan, T., Rose, P., and Fincham, F. (2004). Curiosity and exploration: Facilitating positive subjective experiences and personal growth opportunities. Journal of Personality Assessment, 82(3), 291–305.Google Scholar
Kato, M.P., White, R.W., Teevan, J., and Dumais, S.T. (2013). Clarifications and question specificity in synchronous social Q&A. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 913–918). ACM Press.
Kato, M.P., Yamamoto, T., Ohshima, H., and Tanaka, K. (2014). Cognitive search intents hidden behind queries: a user study on query formulations. In Proceedings of the companion publication of the international conference on the World Wide Web (pp. 313–314). International World Wide Web Conferences Steering Committee.
Kautz, H., Selman, B., and Milewski, A. (1996). Agent amplified communication. In Proceedings of the AAAI conference on artificial intelligence (pp. 3–9). AAAI Press.
Kautz, H., Selman, B., and Shah, M. (1997). Referral-Web: Combining social networks and collaborative filtering. Communications of the ACM, 40(3), 63–65.Google Scholar
Kay, J. (2006). Scrutable adaptation: Because we can and must. In Proceedings of the international conference on adaptive hypermedia and adaptive Web-based systems (pp. 11–19). Springer Verlag.
Kay, K.N., Naselaris, T., Prenger, R.J., and Gallant, J.L. (2008). Identifying natural images from human brain activity. Nature, 452(7185), 352–355.Google Scholar
Kayaaslan, E., Cambazoglu, B.B., Blanco, R., Junqueira, F.P., and Aykanat, C. (2011). Energy-price-driven query processing in multi-center web search engines. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 983–992). ACM Press.
Kazai, G. (2011). In search of quality in crowdsourcing for search engine evaluation. In Proceedings of the European conference on information retrieval (pp. 165–176). SpringerBerlin Heidelberg.
Keim, D. (2001). Visual exploration of large data sets. Communications of the ACM, 44(8), 39–44.Google Scholar
Kekäläinen, J. (2005). Binary and graded relevance in IR evaluations: Comparison of the effects on ranking of IR systems. Information Processing and Management, 41(5), 1019–1033.Google Scholar
Kellar, M., Watters, C., and Shepherd, M. (2007). A field study characterizing Web-based information-seeking tasks. Journal of the American Society for Information Science and Technology, 58(7), 999–1018.Google Scholar
Kellar, M., Watters, C., and Shepherd, M. (2006). A goal-based classification of web information tasks. In Proceedings of the American Society for Information Science and Technology, 43(1), 1–22.Google Scholar
Keller, M., Mühlschlegel, P., and Hartenstein, H. (2013). Search result presentation: supporting post-search navigation by integration of taxonomy data. In Proceedings of the companion publication of the international conference on the World Wide Web (pp. 1269–1274). ACM Press.
Keller, R., Wolf, S., Chen, J., Rabinowitz, J., and Mathe, N.A. (1997). Bookmarking service for organizing and sharing URLs. Computer Networks and ISDN Systems, 29(8–13), 1103–1114.Google Scholar
Kelley, K. and Preacher, K.J. (2012). On effect size. Psychological methods, 17(2), 137.Google Scholar
Kelly, D. (2004). Understanding implicit feedback and document preference: A naturalistic user study. Ph.D. Dissertation, Rutgers University.
Kelly, D. (2009). Methods for evaluating interactive information retrieval systems with users. Foundations and Trends in Information Retrieval, 3(1–2), 1–224.Google Scholar
Kelly, D. and Belkin, N.J. (2001). Reading time, scrolling and interaction: Exploring sources of user preferences for relevance feedback during interactive information retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 408–409). ACM Press.
Kelly, D. and Belkin, N.J. (2004). Display time as implicit feedback: Understanding task effects. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 377–384). ACM Press.
Kelly, D. and Cool, C. (2002). The effects of topic familiarity on information search behavior. In Proceedings of the joint conference on digital libraries (pp. 74–75).
Kelly, D., Cushing, A., Dostert, M., Niu, X., and Gyllstrom, K. (2010). Effects of popularity and quality on the usage of query suggestions during information search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 45–54). ACM Press.
Kelly, D., Dollu, V.D., and Fu, X. (2005). The loquacious user: A document-independent source of terms for query expansion. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 457–464). ACM Press.
Kelly, D. and Gyllstrom, K. (2011). An examination of two delivery modes for interactive search system experiments: Remote and laboratory. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1531–1540). ACM Press.
Kelly, D. and Teevan, J. (2003). Implicit feedback for inferring user preference. SIGIR Forum, 37 (2), 18–28.Google Scholar
Kelly, G.A. (1963). Theory of Personality: The Psychology of Personal Constructs. New York: W.W. Norton.
Kemp, C. and Ramamohanarao, K. (2002). Long-term learning for web search engines. In Proceedings of the European conference on principles and practice of knowledge discovery in databases (pp. 263–274).
Kerne, A., Koh, E., Smith, S., Webb, A., and Dworaczyk, B. (2008). combinFormation: Mixed-initiative composition of image and text surrogates promotes information discovery. ACM Transactions on Information Systems, 27(1), 5. ACM Press.
Kerne, A., Webb, AM., Smith, S.M., Linder, R., Lupfer, N., Qu, Y., Moeller, J., and Damaraju, S. (2014). Using metrics of curation to evaluate information-based ideation. ACM Transactions on Computer-Human Interaction, 21(3), 14. ACM Press.
Keskustalo, H., Järvelin, K., and Pirkola, A. (2008). Evaluating the effectiveness of relevance feedback based on a user simulation model: Effects of a user scenario on cumulated gain value. Information Retrieval, 11(3), 209–228.Google Scholar
Khan, K. and Locatis, C. (1998). Searching through the cyber-space: The effects of link display and link density on information retrieval from hypertext on the World Wide Web. Journal of the American Society for Information Science and Technlogy, 49(2), 176–182.Google Scholar
Kiciman, E. (2012). OMG, I have to tweet that! A study of factors that influence tweet rates. In Proceedings of the international AAAI conference on weblogs and social media.
Kiciman, E. and Richardson, M. (2015). Towards decision support and goal achievement: Identifying action-outcome relationships from social media. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 547–556). ACM Press.
Kim, J. (2006). Task as a predictable indicator of information seeking behavior on the Web. Ph.D. Disseration, Rutgers University.
Kim, J., Oard, D.W., and Romanik, K. (2000). Using Implicit Feedback for User Modelling in Internet and Intranet Searching. College Park: College of Library and Information Services, University of Maryland.
Kim, Y.M. and Rieh, S.Y. (2005). Dual-task performance as a measure for mental effort in library searching and web searching. In Proceedings of the annual meeting of the American society for information science and technology, 42(1).Google Scholar
Kim, J.Y., Collins-Thompson, K., Bennett, P.N., and Dumais, S.T. (2012). Characterizing web content, user interests, and search behavior by reading level and topic. In Proceedings of the ACM WSDM international conference on Web search and data mining (pp. 213–222). ACM Press.
Kim, S. and Soergel, D. (2005). Selecting and measuring task characteristics as independent variables. In Proceedings of the American society for information science and technology, 42(1).Google Scholar
Kim, Y., Hassan, A., White, R.W., and Wang, Y.M. (2013). Playing by the rules: Mining query associations to predict search performance. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 133–142). ACM Press.
Kim, Y., Hassan, A., White, R.W., and Zitouni, I. (2014). Modeling dwell time to predict click-level satisfaction. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 193–202). ACM Press.
Kim, K., Turner, S.A., and Pérez-Quiñones, M.A. (2009). Requirements for electronic note taking systems: A field study of note taking in university classrooms. Education and Information Technologies, 14(3), 255–283.Google Scholar
King, G. (2011). Ensuring the data-rich future of the social sciences. Science, 331(6018), 719–721.Google Scholar
Kintsch, W. (1998). Comprehension: A Paradigm for Cognition. New York: Cambridge University Press.
Kirk, T. (1974). Problems in library instruction in four-year colleges. In Lubans, J., Jr. (Ed.), Educating the Library User, New York: R. R. Bowker (pp. 83–103).
Kiseleva, J., Crestan, E., Brigo, R., and Ditte, R. (2014). Modelling and detecting changes in user satisfaction. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1449–1458). ACM Press.
Kittur, A., Chi, E.H., and Suh, B. (2008). Crowdsourcing user studies with Mechanical Turk. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 453–456). ACM Press.
Kitzinger, J. (1995). Qualitative research. Introducing focus groups. British medical journal, 311(7000), 299.Google Scholar
Klein, G., Moon, B. and Hoffman, R.F. (2006). Making sense of sensemaking I: Alternative perspectives. IEEE Intelligent Systems, 21(4), 70–73. IEEE.Google Scholar
Klein, G., Orasanu, J., Calderwood, R., and Zsambok, C.E. (1993). Decision Making in Action: Models and Methods: Norwood, NJ: Ablex Publishing Co.
Kleinberg, J. (2000). The small-world phenomenon: An algorithmic perspective. In Proceedings of the ACM symposium on theory of computing (pp. 163–170). ACM Press.
Kleinberg, J.M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632. ACM Press.Google Scholar
Klepeis, N.E., William, C.N., Wayne, R.O., John, P.R., Andy, M.T., Paul, S., Joseph, V.B., Stephen, C.H., and William, H.E. (2001). The national human activity pattern survey. Journal of Exposure Analysis and Environmental Epidemiology, 11(3), 231–252.Google Scholar
Knight, F.H. (1924). Some fallacies in the interpretation of social cost. The Quarterly Journal of Economics, 38(4), 582–606.Google Scholar
Kobsa, A. (2007). Privacy-enhanced personalization. Communications of the ACM, 50(8), 24–33.Google Scholar
Kodagoda, N. and Wong, B.L.W. (2008). Effects of low and high literacy on user performance in information search and retrieval. In Proceedings of the British HCI group annual conference on people and computers (pp. 173–181). British Computer Society.
Koenemann, J. and Belkin, N.J. (1996). A case for interaction: A study of interactive information retrieval behavior and effectiveness. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 205–212). ACM Press.
Kohavi, R., Henne, R.M., and Sommerfield, D. (2007). Practical guide to controlled experiments on the web: Listen to your customers not to the hippo. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 959–967). ACM Press.
Kohavi, R. and Longbotham, R. (2007). Online experiments: Lessons learned. Computer, 40(9), 103–105.Google Scholar
Kohavi, R., Longbotham, R., Sommerfield, , D., and Henne, R.M. (2009). Controlled experiments on the web: Survey and practical guide. Data Mining and Knowledge Discovery, 18(1), 140–181.Google Scholar
Kohavi, R., Longbotham, R., and Walker, T. (2010). Online experiments: Practical lessons. Computer, 43(9), 82–85.Google Scholar
Kohavi, R. and Longbotham, R. (2011). Unexpected results in online controlled experiments. ACM SIGKDD Explorations Newsletter, 12(2), 31–35.Google Scholar
Kohn, A. (1989). Fortune or Failure: Missed Opportunities and Chance Discoveries. Cambridge, MA: Blackwell.
Kolb, D. (1985). Learning Style Inventory: Self Scoring Inventory and Interpretation Booklet. Boston, MA: McBer and Company.
Komlodi, A. (2002). Search History for User Support in Information-Seeking Interfaces. Unpublished doctoral dissertation, University of Maryland, College Park.
Komlodi, A. (2002). The role of interaction histories in mental model building and knowledge sharing in the legal domain. Journal of Universal Computer Science, 8(5), 557–566.Google Scholar
Komlodi, A., Soergel, D., and Marchionini, G. (2006). Search histories for user support in user interfaces. Journal of the American Society for Information Science and Technology, 57(6), 803–807.Google Scholar
Komogortsev, O.V., Ryu, Y.S., Koh, D.H., and Gowda, S.M. (2009). Instantaneous saccade driven eye gaze interaction. In Proceedings of the international conference on advances in computer entertainment technology (pp. 140–147). ACM Press.
Kong, W., Aktolga, E., and Allan, J. (2013). Improving passage ranking with user behavior information. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1999–2008). ACM Press.
Konstan, J.A. and Riedl, J. (2012). Recommender systems: From algorithms to user experience. In Proceedings of the conference on user modeling and user-adapted interaction (pp. 1–23). Springer Verlag.
Kopalle, P.K. and Neslin, S. (2001). The economic viability of frequency reward programs in a strategic competitive environment. Tuck School of Business at Dartmouth Working Paper (01–02).
Korfhage, R.R. (1991). To see or not to see – is that the query? In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 134–141), ACM Press.
Korolova, A., Kenthapadi, K., Mishra, N., and Ntoulas, A. (2009). Releasing search queries and clicks privately. In Proceedings of the international conference on World Wide Web (pp. 171–180). ACM Press.
Kotov, A., Bennett, P. N., White, R. W., Dumais, S.T., and Teevan, J. (2011). Modeling and analysis of cross-session search tasks. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 5–14). ACM Press.
Kozierok, R. and Maes, P. (1993). A learning interface agent for scheduling meetings. In Proceedings of the ACM IUI conference on intelligent user interfaces (pp. 81–88). ACM Press.
Kraft, R. and Zien, J. (2004). Mining anchor text for query refinement. In Proceedings of the international conference on the World Wide Web (pp. 666–674).
Kramer, A.D., Guillory, J.E., and Hancock, J.T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. In Proceedings of the National Academy of Sciences, 111(24), 8788–8790.Google Scholar
Krathwohl, D.R. (2002). A revision of Bloom's taxonomy: An overview. Theory into practice, 41(4), 212–218.Google Scholar
Krause, A. and Horvitz, E. (2008). A utility-theoretic approach to privacy and personalization. In Proceedings of the AAAI conference on artificial intelligence (pp. 1181–1188). AAAI Press.
Krebs, J.R. and Davies, N.B. (1989). An Introduction to Behavioral Ecology. Oxford: Blackwell Scientific Publications.
Krueger, R.A. (2009). Focus Groups: A Practical Guide for Applied Research. Thousand Oaks, CA: Sage.
Krumm, J. and Horvitz, E. (2006). Predestination: Inferring destinations from partial trajectories. In Proceedings of the conference on ubiquitous computing (pp. 243–260). SpringerBerlin Heidelberg.
Krumm, J. and Rouhana, D. (2013). Placer: Semantic place labels from diary data. In Proceedings of the ACM international joint conference on pervasive and ubiquitous computing (pp. 163–172). ACM.
Kuhlthau, C. (1988). Developing a model of the library search process: Cognitive and affective aspects. Retrieval Quarterly, 28(2), 232–242.Google Scholar
Kuhlthau, C. (1991). Inside the search process: Information seeking from the user's perspective. Journal of the American Society for Information Science, 42(5), 361–371.Google Scholar
Kuhlthau, C.C. (1993). A principle of uncertainty for information seeking. Journal of documentation, 49(4), 339–355.Google Scholar
Kuhn, T.S. (1970). The Structure of Scientific Revolutions. Chicago: Chicago University Press.
Kules, B. (2005). Supporting creativity with search tools. In Proceedings of NSF Workshop on Creativity Support Tools (pp. 53–64).
Kules, B., and Shneiderman, B. (2008). Users can change their web search tactics: Design guidelines for categorized overviews. Information Processing and Management, 44(2), 463–484.Google Scholar
Kulkarni, A., Teevan, J., Svore, K.M., and Dumais, S.T. (2011). Understanding temporal query dynamics. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 167–176). ACM Press.
Kumar, M., Garfinkel, T., Boneh, D., and Winograd, T. (2007). Reducing shoulder-surfing by using gaze-based password entry. In Proceedings of the symposium on usable privacy and security (pp. 13–19). ACM Press.
Kumar, R., Novak, J., Pang, B., and Tomkins, A. (2007). On anonymizing query logs via token-based hashing. In Proceedings of the international conference on the World Wide Web (pp. 629–638).Google Scholar
Kumar, V., Furuta, R., and Allen, R. (1998). Metadata visualization for digital libraries: Interactive timeline editing and review. In Proceedings of the ACM conference on digital libraries (pp. 126–133).Google Scholar
Kumaran, G. and Carvalho, V.R. (2009). Reducing long queries using query quality predictors. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 564–571). ACM Press.
Kumaran, G., Jones, R., and Madani, O. (2005). Biasing web search results for topic familiarity. In Proceedings of the ACM conference on information and knowledge management (pp. 271–272). ACM Press.
Kumpulainen, S. and Järvelin, K. (2010). Information interaction in molecular medicine: Integrated use of multiple channels. In Proceedings of the IIiX symposium on information interaction in context (pp. 95–104). ACM Press.
Kuniavsky, M. (2003). Observing the User Experience: A Practioner's Guide for User Research. San Francisco, CA: Morgan Kaufman.
Kurtenbach, G. and Hulteen, E.A. (1990). Gestures in human-computer communication. In Laurel, B. (ed.) The art of human-computer interface design (pp. 309–317). Boston, MA: Addison-Wesley Longman Publishing Co.
Kwasnik, B. (1989). How a personal document's intended use or purpose affects its classification in an office. ACM SIGIR Forum, 23(SI), 207–210. ACM Press.
Kwasnik, B.H. (1999). The role of classification in knowledge representation and discovery. Library Trends, 48(1), 22–47.Google Scholar
Lagergren, E. and Over, P. (1998). Comparing interactive information retrieval systems across sites: the TREC-6 interactive track matrix experiment. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 164–172). ACM Press.
Lagun, D. and Agichtein, E. (2011). Viewser: Enabling large-scale remote user studies of web search examination and interaction. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 365–374). ACM Press.
Lagun, D., Ageev, M., Guo, Q., and Agichtein, E. (2014a). Discovering common motifs in cursor movement data for improving web search. In Proceedings of the ACM WSDM conference on Web search and data mining (pp. 183–192). ACM Press.
Lagun, D., Hsieh, C. H., Webster, D., and Navalpakkam, V. (2014b). Towards better measurement of attention and satisfaction in mobile search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 113–122). ACM Press.
Lalmas, M., O'Brien, H., and Yom-Tov, E. (2014). Measuring user engagement. Synthesis Lectures on Information Concepts, Retrieval, and Services, 6(4), 1–132.Google Scholar
Lalmas, M. and Ruthven, I. (1998). Representing and retrieving structured documents using the dempster-shafer theory of evidence: modelling and evaluation. Journal of Documentation, 54(5), 529–565.CrossRefGoogle Scholar
Lam, H., Russell, D., Tang, D., and Munzner, T. (2007). Session viewer: Visual exploratory analysis of web session logs. In Proceedings of the IEEE symposium on visual analytics science and technology (pp. 147–154). IEEE.
Lam-Adesina, A.M. and Jones, G.J.F. (2001). Applying summarization techniques for term selection in relevance feedback. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1–9). ACM Press.
Lambert, D. and Pregibon, D. (2007). More bang for their bucks: Assessing new features for online advertisers. In Proceedings of the international workshop on data mining and audience intelligence for advertising (pp. 7–15). ACM Press.
Landauer, T.K. (1996). The Trouble with Computers. Cambridge, MA: The MIT Press,
Landauer, T.K. (2002). On the computational basis of learning and cognition: Arguments from LSA. The Psychology of Learning and Motivation, 41, 43–84.Google Scholar
Landauer, T., Egan, D., Remde, J., Lesk, M., Lochbaum, C., and Ketchum, D. (1993). Enhancing the usability of text through computer delivery and formative evaluation: the SuperBook project. In McKnight, C., Dillon, A., and Richardson, J. (Eds.) Hypertext: A Psychological Perspective (pp. 71–136). New York: Ellis Horwood.
Landgren, J. (2006). Making action visible in time-critical work. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 201–210). ACM.
Landy, F.J., Rastegary, H., Thayer, J., and Colvin, C. (1991). Time urgency: The construct and its measurement. Journal of Applied Psychology, 76(5), 644.Google Scholar
Lanier, J. (2013). Who owns the future?New York: Simon and Schuster.
Larsen, J.T., Norris, C.J., and Cacioppo, J.T. (2003). Effects of positive and negative affect on electromyographic activity over zygomaticus major and corrugator supercilii. Psychophysiology, 40(5), 776–785.Google Scholar
Lashkari, T., Metral, M., and Maes, P. (1994). Collaborative interface agents. In Proceedings of the AAAI conference on artificial intelligence (pp. 444–449). AAAI Press.
Lauckner, C. and Hsieh, G. (2013). The presentation of health-related search results and its impact on negative emotional outcomes. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 333–342). ACM Press.
Laxman, S., Tankasali, V., and White, R.W. (2008). Stream prediction using a generative model based on frequent episodes in event sequences. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 453–461). ACM Press.
Lazer, D., Kennedy, R., King, G., and Vespigiani, A. (2014). The parable of google flu: Traps in big data analysis. Science, 343(6176), 1203–1205.Google Scholar
Lazer, D., Pentland, A.S., Adamic, L., Aral, S., Barabasi, A.L., Brewer, D., and Van Alstyne, M. (2009). Life in the network: The coming age of computational social science. Science, 323(5915), 721.Google Scholar
Lazonder, A.W., Biemans, H.J.A., and Worpeis, I.G.J.H. (2000). Differences between novice and experienced users in searching information on the World Wide Web. Journal of the American Society for Information Science and Technology, 51(6), 576–581.Google Scholar
Lebanon, G. (2014). Challenges and new directions in recommendation systems. Practice and experience talk at the conference on web search and data mining.
Lee, U., Liu, Z., and Cho, J. (2005). Automatic identification of user goals in web search. In Proceedings of the international conference on the World Wide Web (pp. 391–400). ACM.
Lehmann, S., Schwanecke, U., and Dörner, R. (2010). Interactive visualization for opportunistic exploration of large document collections. Information Systems, 35(2), 260–269.Google Scholar
Lehmann, J., Lalmas, M., Yom-Tov, E., and Dupret, G. (2012). Models of user engagement. In Proceedings of the conference on user modeling, adaptation, and personalization (pp. 164–175). SpringerBerlin Heidelberg.
Lei, Y., Uren, V., and Motta, E. (2006). Semsearch: A search engine for the semantic web. In Proceedings of the international conference on knowledge engingeering and knowledge management (pp. 238–245). SpringerBerlin Heidelberg.
Leigh, R.J. and Zee, D.S. (1999). The Neurology of Eye Movements (Vol. 90). New York: Oxford University Press.
Leiva, L. (2011). Restyling website design via touch-based interactions. In Proceedings of Mobile HCI (pp. 599–604).
Leiva, L.A. and Huang, J. (2015). Building a better mousetrap: Compressing mouse cursor activity for web analytics. Information Processing and Management, 51(2), 114–129.Google Scholar
Lesk, M., Cutting, D., Pedersen, J., Noreault, T., and Koll, M. (1997). Real life information retrieval (panel): commercial search engines. ACM SIGIR Forum, 31(SI), 333. ACM Press.Google Scholar
Leskovec, J., Dumais, S., and Horvitz, E. (2007). Web projections: Learning from contextual subgraphs of the web. In Proceedings of the international conference on the World Wide Web (pp. 471–480). ACM Press.
Lettner, F. and Holzmann, C. (2012). Heat maps as a usability tool for multi-touch interaction in mobile applications. In Proceedings of the international conference on mobile and ubiquitous multimedia (p. 49). ACM Press,
Leuthardt, E.C., Schalk, G., Wolpaw, J.R., Ojemann, J.G., and Moran, D.W. (2004). A brain–computer interface using electrocorticographic signals in humans. Journal of Neural Engineering, 1(2), 63.Google Scholar
Leiva, L.A. (2011). Restyling website design via touch-based interactions. In Proceedings of the international conference on human computer interaction with mobile devices and services (pp. 599–604). ACM Press.
Lewandowsky, S., Ecker, U., Seifert, C.M., Schwarz, N., and Cook, J. (2012). Misinformation and its correction continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106–131.Google Scholar
Lewis, M. (2004). The influence of loyalty programs and short-term promotions on customer retention. Journal of Marketing Research, 41(3), 281–292.Google Scholar
Li, D., Babcock, J., and Parkhurst, D. (2006). openEyes: A low-cost head-mounted eye-tracking solution. In Proceedings of the symposium on eye tracking research and applications (pp. 95–100). ACM Press.
Li, I., Forlizzi, J., and Dey, A. (2010). Know thyself: Monitoring and reflecting on facets of one's life. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 4489–4492). ACM Press.
Li, I., Nichols, J., Lau, T., Drews, C., and Cypher, A. (2010). Here's what i did: Sharing and reusing web activity with ActionShot. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 723–732). ACM Press.
Li, J., Huffman, S., and Tokuda, A. (2009). Good abandonment in mobile and PC internet search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 43–50). ACM Press.
Li, L., Chen, S., Kleban, J., and Gupta, A. (2015). Counterfactual estimation and optimization of click metrics in search engines: a case study. In Proceedings of the international conference on the World Wide Web companion (pp. 929–934). International World Wide Web Conferences Steering Committee.
Li, L., Chu, W., Langford, J., and Wang, X. (2011). Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In Proceedings of the ACM WSDM conference on Web search and data mining (pp. 297–306). ACM Press.
Li, Y. (2010). Gesture search: A tool for fast mobile data access. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 87–96). ACM Press.
Liang, R.H. and Ouhyoung, M. (1998). A real-time continuous gesture recognition system for sign language. In Proceedings of IEEE international conference on automatic face and gesture recognition (pp. 558–567). IEEE.
Liao, Z., Song, Y., He, L.W., and Huang, Y. (2012). Evaluating the effectiveness of search task trails. In Proceedings of the international conference on the World Wide Web (pp. 489–498). ACM Press.
Licklider, J.C.R. (1960). Man–computer symbiosis. IRE Transactions on Human Factors in Electronics, v. HFE-1, pp. 4–11.
Lieberman, H. (1995). Letizia: An agent that assists web browsing. In Proceedings of the international joint conference on artificial intelligence (pp. 475–480).
Lieberman, H., Fry, C., and Weitzman, L. (2001). Exploring the web with reconnaissance agents. Communications of the ACM, 44(7), 69–75.Google Scholar
Liebling, D.J., Bennett, P.N., and White, R.W. (2012). Anticipatory search: Using context to initiate search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1035–1036). ACM Press.
Lin, J. and Smucker, M.D. (2008). How do users find things with PubMed? Towards automatic utility evaluation with user simulations. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 19–26). ACM Press.
Ling, K., Beenen, G., Ludford, P., Wang, X., Chang, K., Li, X, Cosley, D., Frankowski, D., Terveen, L., Rashid, A.M., Resnick, P., and Kraut, R. (2005). Using social psychology to motivate contributions to online communities. Journal of Computer-Mediated Communication, 10(4), 10.Google Scholar
Liu, B. and Oard, D.W. (2006). One-sided measures for evaluating ranked retrieval effectiveness with spontaneous conversational speech. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 673–674). ACM Press.
Liu, C., White, R.W., and Dumais, S. (2010). Understanding web browsing behaviors through Weibull analysis of dwell time. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 379–386).Google Scholar
Liu, F., Yu, C., and Meng, W. (2002). Personalized web search by mapping user queries to categories. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 558–565). ACM Press.
Liu, F., Yu, C., and Meng, W. (2004). Personalized web search for improving retrieval effectiveness. IEEE Transactions on Knowledge and Data Engineering, 16(1), 28–40.Google Scholar
Liu, J., Belkin, N.J., Zhang, X., and Yuan, X. (2013). Examining users’ knowledge change in the task completion process. Information Processing and Management, 49(5), 1058–1074.Google Scholar
Liu, J., Cole, M.J., Liu, C., Bierig, R., Gwizdka, J., Belkin, N.J., Zhang, J., and Zhang, X. (2010a). Search behaviors in different task types. In Proceedings of the Joint Conference on Digital Libraries (pp. 69–78).
Liu, J., Gwizdka, J., Liu, C., and Belkin, N.J. (2010b). Predicting task difficulty for different task types. In Proceedings of the American Society for Information Science and Technology, 47(1), 1–10.Google Scholar
Liu, Q., Agichtein, E., Dror, G., Maarek, Y., and Szpektor, I. (2012). When web search fails, searchers become askers: Understanding the transition. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 801–810). ACM Press.
Liu, T.Y. (2009). Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3), 225–331.Google Scholar
Liu, X., Croft, W.B., and Koll, M. (2005). Finding experts in community-based question-answering services. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 315–316). ACM Press.
Liu, Y., Bian, J., and Agichtein, E. (2008). Predicting information seeker satisfaction in community question answering. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 483–490). ACM Press.
Livne, A., Gokuldas, V., Teevan, J., Dumais, S.T., and Adar, E. (2014). CiteSight: Supporting contextual citation recommendation using differential search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 807–816). ACM Press.
Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75–98.Google Scholar
Loizides, F. and Buchanan, G.R. (2008). The myth of find: User behaviour and attitudes towards the basic search feature. In Proceedings of the Joint Conference on Digital Libraries (pp. 48–51).
London, S. (1995). Collaboration and Community. Retrieved from http://scottlondon.com/reports/ppcc.html
Lopatovska, I. and Arapakis, I. (2011). Theories, methods and current research on emotions in library and information science, information retrieval and human–computer interaction. Information Processing and Management, 47(4), 575–592.Google Scholar
Lopez, S.J. and Snyder, C.R. (2011). The Oxford Handbook of Positive Psychology. Oxford: Oxford University Press.
Lorigo, L., Haridasan, M., Brynjarsdóttir, H., Xia, L., Joachims, T., Gay, G., Granka, L. A., Pellacini, F., and Pan, B. (2008). Eye tracking and online search: Lessons learned and challenges ahead. Journal of the American Society for Information Science and Technology, 59(7), 1041–1052.Google Scholar
Low, Y., Agarwal, D., and Smola, A.J. (2011). Multiple domain user personalization. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 123–131). ACM Press.
Lowe, D.G. (1999). Object recognition from local scale-invariant features. In Proceedings of the IEEE conference on computer vision (pp. 1150–1157). IEEE.
Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. (2013a). Modeling and predicting the task-by-task behavior of search engine users. In Proceedings of the conference on open research areas in information retrieval (pp. 77–84).
Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. (2013b). Discovering tasks from search engine query logs. ACM Transactions on Information Systems, 31(3), 14.Google Scholar
Ludford, P.J., Cosley, D., Frankowski, D., and Terveen, L. (2004). Think different: Increasing online community participation using uniqueness and group dissimilarity. In Proceedings of ACM SIGCHI conference on human factors in computing systems (pp. 631–638). ACM Press.
Ludford, P. J., Priedhorsky, R., Reily, K., and Terveen, L. (2007). Capturing, sharing, and using local place information. In Proceedings of ACM SIGCHI conference on human factors in computing systems (pp. 1235–1244). ACM Press.
Lymberopoulos, D., Zhao, P., Konig, C., Berberich, K., and Liu, J. (2011). Location-aware click prediction in mobile local search. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 413–422). ACM Press.
Lynch, K. (1960). The Image of the City. Cambridge MA: MIT Press.
Lv, Y., Lymberopoulos, D., and Wu, Q. (2012). An exploration of ranking heuristics in mobile local search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 295–304). ACM Press.
MacArthur, R.H. and Pianka, E.R. (1966). Optimal use of a patchy environment. The American Naturalist, 100, 603–609.Google Scholar
Macdonald, C. and White, R.W. (2009). Usefulness of click-through data in expert search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 816–817). ACM Press.CrossRef
MacKay, D. (1960). What makes a question? The Listener, May 5, 789–790.
MacKay, D.M. (1969). Information Mechanism and Meaning. Boston, MA: MIT Press.
MacKenzie, I.S. (1992). Fitts' law as a research and design tool in human-computer interaction. Human-Computer Interaction, 7(1), 91–139.Google Scholar
MacKenzie, I.S. and Zhang, X. (2008). Eye typing using word and letter prediction and a fixation algorithm. In Proceedings of the symposium on eye tracking research and applications (pp. 55–58). ACM Press.
Mackinlay, J.D., Rao, R., and Card, S.K. (1995). An organic user interface for searching citation links. In Proceedings of the ACM SIGCHI conference on, human factors in computing systems (pp. 67–73). ACM Press.
MacQueen, J.B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the symposium on math, statistics, and probability (pp. 281–297).
Madsen, M., El Kaliouby, R., Goodwin, M., and Picard, R. (2008). Technology for just-in-time in-situ learning of facial affect for persons diagnosed with an autism spectrum disorder. In Proceedings of the ACM SIGACCESS conference on computers and accessibility (pp. 19–26). ACM Press.
Maekawa, T., Hara, T., and Nishio, S.A. (2006). Collaborative Web browsing system for multiple mobile users.In Proceedings of the IEEE conference on pervasive computing and communications (pp. 22–35). IEEE.
Maes, P. (1994). Agents that reduce work and information overload. Communications of the ACM, 37(7), 30–40.Google Scholar
Maglio, P.P., Barrett, R., Campbell, C.S., and Selker, T. (2000). SUITOR: An attentive information system. In Proceedings of the ACM IUI conference on intelligent user interfaces (pp. 169–176). ACM Press.
Maior, H., Pike, M., Wilson, M., and Sharples, S. (2013). Directly evaluating the cognitive impact of search user interfaces: a two-pronged approach with fNIRS. In Proceedings of EuroHCIR (pp. 43–46). ACM Press.
Majaranta, P. and Räihä, K.J. (2002). Twenty years of eye typing: Systems and design issues. In Proceedings of the symposium on eye tracking research and applications (pp. 15–22). ACM Press.
Malacria, S., Scarr, J., Cockburn, A., Gutwin, C., and Grossman, T. (2013). Skillometers: Reflective widgets that motivate and help users to improve performance. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 321–330). ACM Press.
Malone, T. (1982). What makes computer games fun? ACM SIGSOC Bulletin, 13(2–3), 143. ACM Press.
Malone, T.W. (1981). Toward a theory of intrinsically motivating instruction. Cognitive Science, 5(4), 333–369.CrossRefGoogle Scholar
Malone, T.W. (1983). How do people organize their desks?: Implications for the design of office information systems. ACM Transactions on Information Systems, 1(1), 99–112.Google Scholar
Mander, R., Salomon, G., and Wong, Y.Y. (1992). A ‘pile’ metaphor for supporting casual organization of information. In Proceedings of ACM SIGCHI conference on human factors in computing systems (pp. 627–634). ACM Press.
Manglano, V., Beaulieu, M., and Robertson, S. (1998). Evaluation of interfaces for IRS: Modelling end-user searching behavior. In Proceedings of the colloquium on information retrieval (pp. 137–146).
Mankiw, G. (2010). Principles of Macroeconomics. SouthWestern Cengage Learning.
Mann, S. (1997). Wearable computing: A first step toward personal imaging. Computer, 30(2), 25–32.CrossRefGoogle Scholar
Manning, C., Raghavan, P., and Schütze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press.
Manski, C.F. (2000). Economic analysis of social interactions. The Journal of Economic Perspectives, 14, 115–136.Google Scholar
Marchionini, G. (1989a). Making the transition from print to electronic encyclopaedias: Adaptation of mental models. International journal of man-machine studies, 30(6), 591–618.Google Scholar
Marchionini, G. (1989b). Information-seeking strategies of novices using a full-text electronic encyclopedia. Journal of the American Society for Information Science, 40(1), 54–66.Google Scholar
Marchionini, G. (1995). Information Seeking in Electronic Environments. Cambridge: Cambridge University Press.CrossRef
Marchionini, G. (2004). From information retrieval to information interaction. In Proceedings of the European conference on information retrieval, 1–11.
Marchionini, G. (2006a). Exploratory search: From finding to understanding. Communications of the ACM, 49(4), 41–46.Google Scholar
Marchionini, G. (2006b). Toward human-computer information retrieval. Bulletin of the American Society for Information Science and Technology, June/July.
Marchionini, G. (2011). HCIR: Now the tricky part. Keynote presentation at the symposium on human-computer interaction and information retrieval.
Marchionini, G. and Komlodi, A. (1998). Design of interfaces for information seeking. Annual Review of Information Science and Technology, 33, 89–130.Google Scholar
Marchionini, G. and Shneiderman, B. (1988). Finding facts vs. browsing knowledge in hypertext systems. IEEE Computer, 21(1), 70–80.Google Scholar
Marchionini, G., and White, R. (2007). Find what you need, understand what you find. International Journal of Human-Computer Interaction, 23(3), 205–237.Google Scholar
Marchionini, G. and White, R.W. (2009). Information-seeking support systems. Computer, 42(3), 30–32.Google Scholar
Marchionini, G., Song, Y., and Farrell, R. (2009). Multimedia surrogates for video gisting: Toward combining spoken words and imagery. Information Processing and Management, 45(6), 615–630.Google Scholar
Marcotte, E. (2011). Responsive Web Design. Editions Eyrolles.
Maron, ME. (1988). Probabilistic design principles for conventional and full-text retrieval systems. Information Processing and Management, 24(3), 249–256.Google Scholar
Marshall, A. (2009). Principles of Economics: Abridged Edition. Cosimo Classics.
Marshall, C. and Rossman, G.B. (1999). Designing qualitative research (3rd Ed.). Thousand Oaks: Sage Publications.
Marshall, C.C., ShipmanIII, F.M., and Coombs, J.H. (1994). VIKI: Spatial hypertext supporting emergent structure. In Proceedings of the ACM European conference on hypermedia technology (pp. 13–23). ACM Press.
Martin, F.G. (2012). Will massive open online courses change how we teach?. Communications of the ACM, 55(8), 26–28.Google Scholar
Martins, B., Anastácio, I., and Calado, P. (2010). A machine learning approach for resolving place references in text. In Proceedings of the AGILE conference on geographical information science (pp. 221–236). SpringerBerlin Heidelberg.Google Scholar
Matthijs, N. and Radlinski, F. (2011). Personalizing web search using long term browsing history. In Proceedings of ACM WSDM conference on web search and data mining (pp. 25–34). ACM Press.
Maxwell, D. and Azzopardi, L. (2014). Stuck in traffic: How temporal delays affect search behaviour. In Proceedings of the IIiX symposium on information interaction in context (pp. 155–164). ACM Press.
Mayer, R.E. (1999). Fifty years of creativity research. In Sternberg, R.J. (Ed.). Handbook of Creativity. Cambridge: Cambridge University Press.
McCain, K.W. (1995). Mandating sharing journal policies in the natural sciences. Science Communication, 16(4), 403–431.Google Scholar
McCarthy, J.C., Miles, V.C., and Monk, A.F. (1991). An experimental study of common ground in text-based communication. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 209–215). ACM Press.
McCay-Peet, L. and Toms, E.G. (2010). The process of serendipity in knowledge work. In Proceedings of the IIiX symposium on information interaction in context (pp. 377–382). ACM Press.
McCrae, R.R. and Costa, P.C., Jr. (1987). Validation of the five-factor model across instruments and observers. Journal of Personality and Social Psychology, 52, 81–90.Google Scholar
McDonald, D.W. and Ackerman, M.S. (2000). Expertise recommender: A flexible recommendation architecture. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 231–240). ACM Press.
McKenzie, P. (2003). A model of information practices in accounts of everyday-life information seeking. Journal of Documentation, 59(1), 19–40.Google Scholar
McKenzie, P.J. and Davies, E. (2002). Time is of the essence: Social theory of time and its implications for LIS research. In Proceedings of the annual conference of CAIS/ASCI (pp. 1–13).
McKinney, V., Yoon, K., and Zahedi, F.M. (2002). The measurement of web-customer satisfaction: an expectation and disconfirmation approach. Information systems research, 13(3), 296–315.Google Scholar
Meadow, C.T. (1979). Computer as a search intermediary. Online, 3(3), 54–59.Google Scholar
Mei, Q. and Church, K. (2008). Entropy of search logs: How hard is search? with personalization? with backoff?. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 45–54). ACM Press.
Mei, Q., Liu, C., Su, H., and Zhai, C. (2006). A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In Proceedings of the international conference on the World Wide Web (pp. 533–542). ACM.
Meiss, M.R., Gonçalves, B., Ramasco, J.J., Flammini, A., and Menczer, F. (2010). Agents, bookmarks and clicks: a topical model of web navigation. In Proceedings of the ACM conference on hypertext and hypermedia (pp. 229–234). ACM.
Mellon, C.A. (1986). Library anxiety: A grounded theory and its development. College and Research Libraries, 47(2), 160–165.Google Scholar
Melnik, S., Gubarev, A., Long, J.J., Romer, G., Shivakumar, S., Tolton, M., and Vassilakis, T. (2010). Dremel: Interactive analysis of web-scale datasets. In Proceedings of the VLDB Endowment, 3(1–2), 330–339.Google Scholar
Melville, P., Mooney, R.J., and Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of the AAAI conference on artificial intelligence (pp. 187–192). AAAI Press.
Merrill, D. and Maes, P. (2007). Augmenting looking, pointing and reaching gestures to enhance the searching and browsing of physical objects. In Proceedings of conference on pervasive computing (pp. 1–18). SpringerBerlin Heidelberg.
Metcalfe, J.E. and Shimamura, A.P. (1994). Metacognition: Knowing About Knowing. The MIT Press.
Metrikov, P., Diaz, F., Lahaie, S., and Rao, J. (2014). Whole page optimization: How page elements interact with the position auction. In Proceedings of the ACM EC conference on economics and computation (pp. 583–600). ACM Press.
Milic-Frayling, N., Jones, R., Rodden, K., Smyth, G., Blackwell, A., and Sommerer, R. (2004). Smartback: Supporting users in back navigation. In Proceedings of the international conference on the World Wide Web (pp. 63–71). ACM Press.
Millen, D.R. and Feinberg, J. (2006). Using social tagging to improve social navigation. In Proceedings of workshop on the social navigation and community based adaptation technologies.
Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.Google Scholar
Miller, G. (1983). Informavores. In Machlup, F. and Mansfield, U. (eds.), The study of information: Interdisciplinary messages (pp. 111–113). Wiley-Interscience.
Milne, G.R. and Culnan, M.J. (2004). Strategies for reducing online privacy risks: Why consumers read (or don't read) online privacy notices. Journal of Interactive Marketing, 18, 24–25.Google Scholar
Mishra, N., White, R.W., Ieong, S., and Horvitz, E. (2014). Time-critical search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 747–756). ACM Press.
Mistry, P. and Maes, P. (2009). SixthSense: a wearable gestural interface. In Proceedings of ACM SIGGRAPH ASIA Sketches (p. 11). ACM Press.
Mistry, P. and Wang, H. (2011). Precursor. http://www.pranavmistry.com/projects/precursor/.
Mitchell, T. (1997). Machine Learning, Burr Ridge, IL: McGraw Hill.
Mitchell, T.M., Caruana, R., Freitag, D., McDermott, J., and Zabowski, D. (1994). Experience with a learning personal assistant. Communications of the ACM, 37(7), 80–91.Google Scholar
Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X., Just, M., and Newman, S. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57(1–2), 145–175.Google Scholar
Mitra, M., Singhal, A., and Buckley, C. (1998). Improving automatic query expansion. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 206–214). ACM Press.
Mizarro, S. and Tasso, C. (2002). Ephemeral and persistent personalization in adaptive information access to scholarly publications on the Web. In Proceedings of the international conference on adaptive hypermedia and adaptive Web based systems (pp. 306–316). SpringerBerlin Heidelberg.
Mizzaro, S. (1997). Relevance: The whole history. Journal of the American Society for Information Science, 48(9), 810–832.Google Scholar
Moffat, A. and Zobel, J. (2008). Rank-biased precision for measurement of retrieval effectiveness. ACM Transactions on Information Systems, 27(1), 2.Google Scholar
Monroe, M., Lan, R., Lee, H., Plaisant, C., and Shneiderman, B. (2013). Temporal event sequence simplification. IEEE Transactionson Visualization and Computer Graphics, 19(2), 2227–2236.Google Scholar
Monsell, S. (2003). Task switching. TRENDS in Cognitive Sciences, 7(3), 134–140.Google Scholar
Montaner, M., Lopez, B., and de la Rosa, J.L. (2002). Developing trust in recommender agents. In Proceedings of the international joint conference on autonomous agents and multiagent (pp. 304–305).
Montañez, G.D., White, R.W., and Huang, X. (2014). Cross-device search. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1669–1678). ACM Press.
Moore, P. (1995). Information problem solving: A wider view of library skills. Contemporary educational psychology, 20(1), 1–31.Google Scholar
Moore, D S. (1997). New pedagogy and new content: The case of statistics. International Statistical Review/Revue Internationale de Statistique, 123–137.
Moraveji, N., Morris, M.R., Morris, D., Czerwinski, M., and Riche, N. (2011a). ClassSearch: Facilitating the development of web search skills through social learning. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1797–1806). ACM Press.
Moraveji, N., Russell, D., Bien, J., and Mease, D. (2011b). Measuring improvement in user search performance resulting from optimal search tips. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 355–363). ACM Press.
Morgan, D.L. (1997). Focus Groups as Qualitative Research (Vol. 16). London: Sage.
Morita, M. and Shinoda, Y. (1994). Information filtering based on user behavior analysis and best match text retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 272–281). ACM Press.
Morris, D., Morris, M.R., and Venolia, G. (2008). SearchBar: A search-centric web history for task resumption and information re-finding. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1207–1216). ACM Press.
Morris, D., Collett, P., Marsh, P., and O'Shaughnessy, M. (1980). Gestures. New York, NY: Stein and Day.
Morris, M.R. (2008). A survey of collaborative web search practices. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1657–1660). ACM Press.
Morris, M.R. (2012). Web on the wall: Insights from a multimodal interaction elicitation study. In Proceedings of the ACM ITS conference on interactive tabletops and surfaces (pp. 95–104). ACM Press.
Morris, M.R. (2013). Collaborative search revisited. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 1181–1192). ACM Press.CrossRef
Morris, M.R. and Horvitz, E. (2007). SearchTogether: An interface for collaborative Web search. In Proceedings of the ACM UIST conference on user interface software and technology (pp. 3–12). ACM Press.
Morris, M.R. and Horvitz, E. (2007). S3: Storable, shareable search. In Proceedings of the conference on Human-Computer Interaction–INTERACT (pp. 120–123). SpringerBerlin Heidelberg.
Morris, M.R., Paepcke, A., and Winograd, T. (2006). TeamSearch: Comparing techniques for co-present collaborative search of digital media. In Proceedings of the IEEE international workshop on horizontal interactive human-computer systems (pp. 97–104). IEEE.
Morris, M.R., Teevan, J., and Panovich, K. (2010). What do people ask their social networks, and why?: A survey study of status message Q&A behavior. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1739–1748). ACM Press.
Morrison, J.B., Pirolli, P., and Card, S.K. (2001). A taxonomic analysis of what World Wide Web activities significantly impact people's decisions and actions. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 163–164). ACM Press.
Moshfeghi, Y. and Jose, J.M. (2013a). On cognition, emotion, and interaction aspects of search tasks with different search intentions. In Proceedings of the international conference on the World Wide Web (pp. 931–942). International World Wide Web Conferences Steering Committee.
Moshfeghi, Y. and Jose, J.M. (2013b). An effective implicit relevance feedback technique using affective, physiological and behavioural features. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 133–142). ACM Press.
Moshfeghi, Y., Pinto, LR., Pollick, F.E., and Jose, J.M. (2013). Understanding relevance: An fMRI study. In Proceedings of the European conference on information retrieval (pp. 14–25). SpringerBerlin Heidelberg.
Moustakis, V. (1997). Do people in HCI use machine learning? In Advances in human factors/ergonomics, 95–98.
Mueller, F., and Lockerd, A. (2001). Cheese: Tracking mouse movement activity on websites, a tool for user modeling. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 279–280). ACM Press.
Mulder, A. (1996). Hand gestures for HCI. hand centered studies of human movement project, Technical Report, 96-1. Simon Fraser University.
Mumford, M.D. (2003). Where have we been, where are we going? Taking stock in creativity research. Creativity Research Journal, 15, 107–120.Google Scholar
Munro, A.J., Höök, K., and Benyon, D. (Eds.). (1999). Social Navigation of Information Space. London, New York: Springer.
Muralidharan, A., Gyongyi, Z., and Chi, E. (2012). Social annotations in web search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1085–1094). ACM Press.
Muramatsu, J. and Pratt, W. (2001). Transparent queries: Investigating users' mental models of search engines. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 217–224). ACM Press.
Murphy, K. (2012). Machine Learning: A Probabilistic Perspective. Boston, MA: MIT Press.
Murray, G.C., Lin, J., and Chowdhury, A. (2006). Identification of user sessions with hierarchical agglomerative clustering. In Proceedings of the American association for information science and technology, 43(1), 1–5.Google Scholar
Murugiah, K., Vallakati, A., Rajput, K., Sood, A., and Challa, N. (2011). Youtube as a source of information on cardiopulmonary resuscitation. Resuscitation, 82(3), 332–334.Google Scholar
Mynatt, B. and Tullio, J. (2001). Inferring calendar event attendance. In Proceedings of the ACM IUI conference on intelligent user interfaces (pp. 121–128). ACM Press.
Nahl, D. (1998). Ethnography of novices' first use of Web search engines: Affective control in cognitive processing. Internet Reference Services Quarterly, 3(2), 51–72.Google Scholar
Nahl, D. and Bilal, D. (Eds.). (2007). Information and Emotion: The Emergent Affective Paradigm in Information Behavior Research and Theory. Medford, NJ: Information Today for ASIST.
Nakazato, M. and Huang, T.S. (2001). 3d Mars: Immersive virtual reality for content-based image retrieval. In Proceedings of the International Conference on Multimedia and Expo (pp. 12). IEEE.
Nallapati, R., Croft, W.B., and Allan, J. (2003). Relevant query feedback in statistical language modeling. In Proceedings on ACM CIKM conference on information and knowledge management (pp. 560–563). ACM Press.
Narayanan, A. and Shmatikov, V. (2008). Robust deanonymization of large sparse datasets. In Proceedings of the IEEE symposium on security and privacy (pp. 111–125). IEEE.
Nardi, B., Whittaker, S., and Bradner, E. (2000). Interaction and outeraction: Instant messaging in action. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 79–88). ACM Press.
Navalpakkam, V. and Churchill, E. (2012). Mouse tracking: Measuring and predicting users' experience of web-based content. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2963–2972). ACM Press.
Navarro-Arribas, G., Torra, V., Erola, A., and Castellà-Roca, J. (2012). User k-anonymity for privacy preserving data mining of query logs. Information Processing and Management, 48(3), 476–487.Google Scholar
Navarro-Prieto, R., Scaife, M., and Rogers, Y. (1999). Cognitive strategies in web searching. In Proceedings of the conference on human factors and the Web (pp. 43–56).
Newell, A., and Simon, H.A. (1972). Human Problem Solving (Vol. 104, No. 9). Englewood Cliffs, NJ: Prentice-Hall.
Nichols, D.M. (1997). Implicit ratings and filtering. In Proceedings of the DELOS workshop on filtering and collaborative filtering (pp. 31–36).
Nielsen, J. (1993). Usability Engineering. San Francisco, CA: Morgan Kaufmann.
Nielsen, J. (April 11, 2011). Incompetent research skills curb users’ problem solving. Alertbox. (available at: http://www.useit.com/alertbox/search-skills.html). Accessed on August 15, 2015.
Nielsen, J. and Levy, J. (1994). Measuring usability – preference vs. performance. Communications of the ACM, 37(4), 66–75.Google Scholar
Nielsen, J. and Loranger, H. (2006). Prioritizing Web Usability. Thousand Oaks, CA: New Riders Publishing.
Nielsen, J. and Norman, D.A. (2000). Web-site usability: usability on the web isn't a luxury. InformationWeek, January 14.
Nielsen, J. and Pernice, K. (2010). Eyetracking Web Usability. Thousand Oaks, CA: New Riders Publishing.
Nijholt, A. and Tan, D. (2008). Brain-computer interfacing for intelligent systems. Intelligent Systems, IEEE, 23(3), 72–79.Google Scholar
Ninio, A. and Bruner, J. (1978). The achievement and antecedents of labelling. Journal of Child Language, 5, 1–15.Google Scholar
Nippert-Eng, C.E. (2008). Home and Work: Negotiating Boundaries through Everyday life. Chicago, IL: University of Chicago Press.
Niu, X. and Kelly, D. (2014). The use of query suggestions as idea tactics during information search. Information Processing and Management, 50(1), 218–234.Google Scholar
Norman, D.A. (1983). Some observations on mental models. Mental models, 1.
Norman, K.A., Polyn, S.M., Detre, G.J., and Haxby, J.V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in cognitive sciences, 10(9), 424–430.Google Scholar
Oard, D. and Kim, J. (2001). Modeling information content using observable behaviors. In Proceedings of the annual meeting of the American society for information science and technology (pp. 38–45).
O'Brien, H.L. and Toms, E.G. (2013). Examining the generalizability of the user engagement scale (UES) in exploratory search. Information Processing and Management, 49(5), 1092–1107.Google Scholar
Obendorf, H., Weinreich, H., Herder, E., and Mayer, M. (2007). Web page revisitation revisited: Implications of a long-term click-stream study of browser usage. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 597–606). ACM Press.
O'Brien, H.L., and Toms, E.G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938–955.Google Scholar
O'Brien, H.L. and Toms, E.G. (2010). The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science and Technology, 61(1), 50–69.Google Scholar
O'Connor, B (1988). Fostering creativity: Enhancing the browsing environment. International Journal of Information Management, 8(3), 203–210.Google Scholar
O'Day, V. and Jeffries, R. (1993). Orienteering in an information landscape: How information seekers get from here to there. In Proceedings of the INTERACT and CHI conference on human factors in computing systems (pp. 438–445). ACM Press.
Oddy, R.N. (1977). Information retrieval through man-machine dialogue. Journal of Documentation, 33(1), 1–14.Google Scholar
Ofek, E., Iqbal, S.T., and Strauss, K. (2013). Reducing disruption from subtle information delivery during a conversation: Mode and bandwidth investigation. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 3111–3120). ACM Press.CrossRef
Oliveira, F.T., Aula, A., and Russell, D.M. (2009). Discriminating the relevance of web search results with measures of pupil size. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2209–2212). ACM Press.
Oliver, P.E. and Marwell, G. (1988). The paradox of group size in collective action: A theory of the critical mass. II. American Sociological Review, 1–8.Google Scholar
Oliver, R.L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research. 17(4), 460–470.Google Scholar
Oliver, R.L. (2010). Satisfaction: A behavioral perspective on the consumer. New York: ME Sharpe.
Olson, J.S., Grudin, J., and Horvitz, E. (2005). A study of preferences for sharing and privacy. In Proceedings of ACM SIGCHI extended abstracts on human factors in computing systems (pp. 1985–1988). ACM Press.
Olson, M.A., Bostic, K., and Seltzer, M.I. (1999). Berkeley DB. In Proceedings of USENIX annual technical conference, FREENIX Track (pp. 183–191).
Olson, M.H. (1983). Remote office work: Changing work patterns in space and time. Communications of the ACM, 26(3), 182–187.Google Scholar
Olston, C. and Chi, E.H. (2003). ScentTrails: Integrating browsing and searching on the Web. ACM Transactions on Computer-Human Interaction, 10(3).Google Scholar
Orne, M.T. (1962). On the social psychology of the psychological experiment: With particular reference to demand characteristics and their implications. American Psychologist, 17, 776–783.Google Scholar
Orne, M.T. (1969). Demand characteristics and the concept of quasi-controls. In Rosenthal, R. and Rosnow, R. (Eds.), Artifact in Behavioral Research. New York: Academic Press, 143–179.
Ortiz, J.R., Zhou, H., Shay, D.K., Neuzil, K.M., Fowlkes, A.L., and Goss, C.H. (2011). Monitoring influenza activity in the United States: A comparison of traditional surveillance systems with Google Flu Trends. PloS One, 6(4), e18687.Google Scholar
Öquist, G. and Goldstein, M. (2003). Towards an improved readability on mobile devices: Evaluating adaptive rapid serial visual presentation. Interacting with Computers, 15(4), 539–558.Google Scholar
Ozmutlu, S. (2006). Automatic new topic identification using multiple linear regression. Information Processing and Management, 42(4), 934–950.Google Scholar
Pace, S. (2004). A grounded theory of the flow experiences of web users. International Journal of Human–Computer Studies, 60(3), 327–363.Google Scholar
Paek, T., Dumais, S., and Logan, R. (2004). WaveLens: A new view onto internet search results. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 727–734). ACM Press.Google Scholar
Palen, L. (2008). Online social media in crisis events. Educause Quarterly, 31(3), 12.Google Scholar
Palen, L., Vieweg, S., Sutton, J., Liu, S.B., and Hughes, A.L. (2007). Crisis informatics: Studying crisis in a networked world. In Proceedings of the international conference on e-social science.
Pandit, S. and Olston, C. (2007). Navigation-aided retrieval. In Proceedings of the international conference on the World Wide Web (pp. 391–400).
Pang, B. and Kumar, R. (2011). Search in the lost sense of query: Question formulation in web search queries and its temporal changes. In Proceedings of the annual meeting of the association for computational linguistics: human language technologies (short papers) (Vol. 2, pp. 135–140). Association for Computational Linguistics.
Pao, M.L. (1993). Term and citation retrieval: A field study. Information Processing and Management, 29(1), 95–112.Google Scholar
Paolacci, G., Chandler, J., and Ipeirotis, P.G. (2010). Running experiments on amazon mechanical turk. Judgment and Decision making, 5(5), 411–419.Google Scholar
Pariser, E. (2011). The Filter Bubble: How the New Personalized Web is Changing What We Read and How We Think. New York, NY: Penguin.
Patel, K., Fogarty, J., Landay, J.A., and Harrison, B.L. (2008). Examining difficulties software developers encounter in the adoption of statistical machine learning. In Proceedings of the AAAI conference on artificial intelligence (pp. 1563–1566). AAAI Press.
Patterson, E.S., Roth, E.M., and Woods, D.D. (2001). Predicting vulnerabilities in computer-supported inferential analysis under data overload. Cognition Technology and Work, 3(4), 224–237.Google Scholar
Paul, M.J. and Dredze, M. (2011). You are what you Tweet: Analyzing Twitter for public health. In Proceedings of the international conference on weblogs and social media (pp. 265–272).
Paul, M.J., White, R.W., and Horvitz, E. (2014). Search and breast cancer: On disruptive shifts of attention over life histories of an illness. Microsoft Research Technical Report: MSR-TR-2014-144.
Pavlovic, V.I., Sharma, R., and Huang, T.S. (1997). Visual interpretation of hand gestures for human-computer interaction: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 677–695.Google Scholar
Pease, A. (2004). The Definitive Guide to Body Language. London: Orion.
Pedersen, J. (2008). The Machine Learned Ranking Story. http://docslide.us/documents/jan-pedersen the-machine-learned-ranking-story.html.
Petajan, E., Bischoff, B., Bodoff, D., and Brooke, N.M. (1988). An improved automatic lipreading system to enhance speech recognition. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 19–25). ACM Press.
Petter, M., Fragoso, V., Turk, M., and Baur, C. (2011). Automatic text detection for mobile augmented reality translation. In Proceedings of the IEEE computer vision workshops (pp. 48–55). IEEE.
Pfeuffer, K., Vidal, M., Turner, J., Bulling, A., and Gellersen, H. (2013). Pursuit calibration: Making gaze calibration less tedious and more flexible. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 261–270). ACM Press.
Pharo, N. (1999). Web information search strategies: A model for classifying Web interaction? In Proceedings of the COLIS conference on conceptions of library and information science (pp. 207–218). Libraries Unlimited.
Pharo, N. and Järvelin, K. (2004). The SST method: A tool for analysing Web information search processes. Information Processing and Management, 40(4), 633–654.Google Scholar
Piaget, J. (1952). The Origins of Intelligence in Children. New York: International Universities Press.
Piaget, J. (1978). La equilibración de las estructuras cognitivas: problema central del desarrollo.
Picard, R.W. (1995). Affective Computing. MIT Tech Report.
Picard, R. (1997). Affective Computing. Cambridge, MA: MIT Press.
Picard, R.W. (2000). Toward computers that recognize and respond to user emotion. IBM Systems Journal, 39(3–4), 705–719.Google Scholar
Pickens, J., Golovchinsky, G., Shah, C., Qvarfordt, P., and Back, M. (2008). Algorithmic mediation for collaborative exploratory search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 315–322). ACM Press.
Pirhonen, A., Brewster, S., and Holguin, C. (2002). Gestural and audio metaphors as a means of control for mobile devices. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 291–298). ACM Press.
Pirolli, P. (1997). Computational models of information scent-following in a very large browsable text collection. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 3–10). ACM Press.
Pirolli, P. (2007). Information Foraging Theory: Adaptive Interaction with Information. Oxford: Oxford University Press.
Pirolli, P. (2007). Exploratory Search Systems. http://web.mac.com/peter.pirolli/Professional/Blog/Entries/2007/5/18_Exploratory_Search_Systems.html. Accessed December 15, 2008.
Pirolli, P. (2009). An elementary social information foraging model. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 605–614). ACM Press.
Pirolli, P. and Card, S. (2005). The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proceedings of International Conference on Intelligence Analysis (Vol. 5, pp. 2–4).Google Scholar
Pirolli, P. and Card, S. (1995). Information foraging in information access environments. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 51–58). ACM Press/Addison-Wesley Publishing Co.
Pirolli, P. and Card, S.K. (1999). Information foraging. Psychological Review, 106, 643–675.CrossRefGoogle Scholar
Pirolli, P., Card, S.K., and Van Der Wege, M.M. (2003). The effects of information scent on visual search in the Hyperbolic Tree Browser. ACM Transactions on Computer-Human Interaction, 10(1), 20–53.Google Scholar
Pirolli, P. and Fu, W.T. (2003). SNIF-ACT: A model of information foraging on the World Wide Web. In Proceedings of the user modeling conference (pp. 45–54). SpringerBerlin Heidelberg.
Pirolli, P., Pitkow, J., and Rao, R. (1996a). Silk from a sow's ear: Extracting usable structures from the Web. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 118–125). ACM Press.
Pirolli, P. and Rao, R. (1996). Table lens as a tool for making sense of data. In Proceedings of the working conference on advanced visual interfaces (pp. 67–80).
Pirolli, P. and Russell, D.M. (2011). Introduction to special issue on sensemaking. In Human Computer Interaction, 26(1), 1–8.Google Scholar
Pirolli, P., Schank, P., Hearst, M., and Diehl, C. (1996b). Scatter/gather browsing communicates the topic structure of a very large text collection. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 213–220). ACM Press.
Pitkow, J., Schutze, H., Cass, T., Cooley, R., Turnbull, D., Edmonds, A., Adar, E., and Breuel, T. (2002). Personalized seach. Communications of the ACM, 45(9), 50–55.Google Scholar
Piwowar, H.A. and Chapman, W.W. (2010). Public sharing of research datasets: A pilot study of associations. Journal of Infometrics, 4(2), 148–156.Google Scholar
Piwowarski, B., Dupret, G., and Jones, R. (2009). Mining user web search activity with layered bayesian networks or how to capture a click in its context. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 162–171). ACM Press.
Plaisant, C. (2004). The challenge of information visualization evaluation. In Proceedings of the working conference on advanced visual interfaces (pp. 109–116).
Plaisant, C., Milash, B., Rose, A., Widoff, S., and Shneiderman, B. (1996). LifeLines: Visualizing personal histories. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 221–227). ACM Press.
Plan, Y. (2011). Compressed Sensing, Sparse Approximation, and Low-rank Matrix Estimation. Unpublished Doctoral Dissertation, California Institute of Technology.
Platt, J. (2000). AutoAlbum: Clustering digital photographs using probabilistic model merging. IEEE workshop on content-based access of image and video libraries, 96–100.
Poddar, A. and Ruthven, I. (2010). The emotional impact of search tasks. In Proceedings of the IIiX conference on information interaction in context (pp. 35–44). ACM Press.
Poh, M.Z., Loddenkemper, T., Reinsberger, C., Swenson, N.C., Goyal, S., Madsen, J.R., and Picard, R.W. (2012). Autonomic changes with seizures correlate with postictal EEG suppression. Neurology, 78(23), 1868–1876.Google Scholar
Polgreen, P.M., Chen, Y., Pennock, D.M., and Forrest, N.D. (2008). Using internet searches for influenza surveillance. Clinical Infectious Diseases, 47(11), 1443–1448.Google Scholar
Pollack, M.E. (1985). Information sought and information provided: An empirical study of user/expert dialogues. ACM SIGCHI Bulletin, 16(4), 155–159. ACM Press.
Potter, M.C. (1976). Short-term conceptual memory for pictures. Journal of Experimental Psychology: Human Learning and Memory, 2(5), 509.Google Scholar
Pousman, Z., Stasko, J. and Mateas, M. (2007). Casual information visualization: depictions of data in everyday life. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1145–1152,Google Scholar
Pratt, W., Hearst, M.A., and Fagan, L.M. (1999). A knowledge-based approach to organizing retrieved documents. In Proceedings of the AAAI conference on artificial intelligence (pp. 80–85). AAAI Press.
Pratt, W., and Yetisgen-Yildiz, M. (2003). LitLinker: Capturing connections across the biomedical literature. In Proceedings of the international conference on knowledge capture (pp. 105–112).
Pretschner, A. and Gauch, S. (1999). Ontology based personalized search. In Proceedings of the IEEE international conference on tools with artificial intelligence (pp. 391–398). IEEE.CrossRef
Psotka, J., Massey, L.D., and Mutter, S.A. (Eds.). (1988). Intelligent Tutoring Systems: Lessons Learned. Hillsdale, NJ: Psychology Press.
Qu, Y. and Furnas, G.W. (2008). Model-driven formative evaluation of exploratory search: A study under a sensemaking framework. Information Processing and Management, 44(2), 534–555.Google Scholar
Qiu, F. and Cho, J. (2006). Automatic identification of user interest for personalized search. In Proceedings of the international conference on the World Wide Web (pp. 727–736). ACM Press.
Quine, W.V. (1953). Identity, ostension, and hypostasis. In Quine, W.V. (Ed.), From a Logical Point of View (pp. 65–79). Cambridge, MA: Harvard University Press.
Quine, W.V. (1969). Natural kinds. In Quine, W.V. (Ed.), Ontological relativity and other essays (pp. 114–138). New York: Columbia University Press.
Quine, W.V. and Quine, W.V.O. (1969). Ontological Relativity and Other Essays (No. 1). New York: Columbia University Press.
Quine, W.V.O. (1980). From a Logical Point of View: 9 Logico-Philosophical Essays (Vol. 9). Cambridge, MA: Harvard University Press.
Quirk, C., Choudhury, P., Gao, J., Suzuki, H., Toutanova, K., Gamon, M., and Cherry, C. (2012). MSR SPLAT, a language analysis toolkit. In Proceedings of the conference of the North American chapter of the association for computational linguistics: human language technologies: Demonstration session (pp. 21–24). Association for Computational Linguistics.
Radinsky, K. and Horvitz, E. (2013). Mining the web to predict future events. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 255–264). ACM Press.
Radinsky, K., Svore, K.M., Dumais, S.T., Shokouhi, M., Teevan, J., Bocharov, A., and Horvitz, E. (2013). Behavioral dynamics on the web: Learning, modeling, and prediction. ACM Transactions on Information Systems, 31(3), 16.Google Scholar
Radlinski, F. and Craswell, N. (2013). Optimized interleaving for online retrieval evaluation. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 245–254). ACM Press.
Radlinski, F., and Dumais, S. (2006). Improving personalized web search using result diversification. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 691–692). ACM Press.
Radlinski, F. and Joachims, T. (2005). Query chains: Learning to rank from implicit feedback. In Proceedings of the ACM SIGKDD conference on knowledge discovery in data mining (pp. 239–248). ACM Press.
Radlinski, F., Kurup, M., and Joachims, T. (2008). How does clickthrough data reflect retrieval quality? In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 43–52). ACM Press.
Rafaeli, S., Raban, D., and Ravid, G. (2007). How social motivation enhances economic activity and incentives in the google answers knowledge sharing market. International Journal of Knowledge and Learning, 3(1), 1–11.Google Scholar
Raman, K., Bennett, P.N., and Collins-Thompson, K. (2013). Toward whole session relevance: Exploring intrinsic diversity in web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 463–472). ACM Press.
Rao, R. and Card, S.K. (1994). The table lens: Merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 318–322). ACM Press.
Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372.Google Scholar
Reas, C. and Fry, B. (2010). Getting Started with Processing. Sebastopol, CA: O'Reilly Media.
Reich, S., Carr, L., De Roure, D., and Hall, W. (1999). Where have you been from here? Trials in hypertext systems. ACM Computing Surveys, 31(4es), 11.Google Scholar
Reid, J. (2000). A task-oriented non-interactive evaluation methodology for information retrieval systems. Information Retrieval, 2(1), 115–129.Google Scholar
Rieh, S.Y. and Xie, H.I. (2006). Analysis of multiple query reformulations on the web: The interactive information retrieval context. Information Processing and Management, 42(3), 751–768.Google Scholar
Reimer, J. (2007). Your ISP may be selling your web clicks. Ars Technica. http://arstechnica.com/news.ars/post/20070315-your-isp-may-be-selling-your-web-clicks.html. Accessed on August 15, 2015.
Reis, S., and Church, K. (2013). Insights into co-located shared mobile search. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 1401–1406). ACM Press.
Reisner, P. (1963). Construction of a growing thesaurus by conversational interaction in a man-machine system. In Proceedings of the annual meeting of the american documentation institute.
Reisner, P. (1966). Evaluation of a ‘growing thesaurus’. Research Paper RC-1662. IBM Watson Research Center. Yorktown Heights, N.Y.
Rekimoto, J. and Ayatsuka, Y. (2000). Cybercode:designing augmented reality environments with visual tags. In Proceedings of designing augmented reality environments (pp. 1–10).
Ren, Y., Tomko, M., Ong, K., Bai, Y.B., and Sanderson, M. (2014). The influence of indoor spatial context on user information behaviours. In Proceedings of the i-ASC 2014 Workshop (p. 13).
Resnick, P. and Varian, H.R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.Google Scholar
Resnick, P. and Virzi, R.A. (1995). Relief from the audio interface blues: Expanding the spectrum of menu, list, and form styles. ACM Transactions on Computer-Human Interaction, 2(2), 145–176.Google Scholar
Rheingold, H. (1991). Virtual Reality: Exploring the Brave New Technologies. Simon and Schuster Adult Publishing Group.
Rheingold, H. (2000). The Virtual Community: Home-steading on the Electronic Frontier. Boston, MA: MIT Press.
Rhodes, B.J. and Starner, T. (1996). Remembrance agent: A continuously running automated information retrieval system. In Proceedings of the international conference on the practical application of intelligent agents and multi agent technology (pp. 487–495).
Ribak, A., Jacovi, M., and Soroka, V. (2002). Ask before you search: peer support and community building with ReachOut. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 126–135). ACM Press.
Ricci, F., Rokach, L., Shapira, B., and Kantor, P.B. (2011). Introduction to recommender systems handbook. Recommender Systems Handbook (pp. 1–35). Springer.
Rich, E. (1979). User modeling via stereotypes. Cognitive Science, 3(4), 329–354.Google Scholar
Rich, E. (1983). Users are individuals: Individualizing user models. International Journal of Man-machine Studies, 18(3), 199–214.Google Scholar
Richardson, M. (2008). Learning about the world through long-term query logs. ACM Transactions on the Web, 2(4), 21.Google Scholar
Richardson, M., Burges, C.J., and Renshaw, E. (2013). McTest: A challenge dataset for the open-domain machine comprehension of text. In Proceedings of the conference on empirical methods in natural language processing (Vol. 1, p. 2).Google Scholar
Richardson, M. and Domingos, P. (2002). The intelligent surfer: Probabilistic combination of link and content information in pagerank. In Proceedings of advances in neural information processing systems (pp. 1441–1448).
Richardson, M., Dominowska, E., and Ragno, R. (2007). Predicting clicks: Estimating the click-through rate for new ads. In Proceedings of the international conference on the World Wide Web (pp. 521–530). ACM Press.
Richardson, M., Prakash, A., and Brill, E. (2006). Beyond PageRank: Machine learning for static ranking. In Proceedings of the international conference on the World Wide Web (pp. 707–715). ACM Press.
Richardson, M. and White, R.W. (2011). Supporting synchronous social Q&A throughout the question lifecycle. In Proceedings of the international conference on the World Wide Web (pp. 755–764). ACM Press.
Rimé, B. (1982). The elimination of visible behaviour from social interactions: Effects on verbal, nonverbal and interpersonal variables. European Journal of Social Psychology, 12(2), 113–129.Google Scholar
Rimé, B., & Schiaratura, L. (1991). Gesture and speech, In Feldman R.S. and Rimé B. (Eds.), Fundamentals of nonverbal behavior (pp. 239–281), Cambridge: Cambridge University Press.
Ringel, M., Cutrell, E., Dumais, S., and Horvitz, E. (2003). Milestones in time: The value of landmarks in retrieving information from personal stores. In Proceedings of Human-Computer Interaction–INTERACT 2003 (pp. 184–191).
Roberts, R.M. (1989). Serendipity: Accidental Discoveries in Science. New York: Wiley VCH.
Robertson, G., Czerwinski, M., Larson, K., Robbins, D.C., Thiel, D., and Van Dantzich, M. (1998). Data mountain: Using spatial memory for document management. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 153–162). ACM Press.
Robertson, S.E. (1977). The probability ranking principle in IR. Journal of Documentation, 33(4), 294–304.Google Scholar
Robins, D. (2000). Shifts of focus on various aspects of user information problems during interactive information retrieval. Journal of the American Society for Information Science, 51(10), 913–928.Google Scholar
Rocchio, J.J. (1971). Relevance feedback in information retrieval. In Salton, G. (Ed.), The SMART retrieval system – experiments in automatic document processing (pp. 313–323). Upper Saddle River, NJ: Prentice-Hall, Inc.
Rodden, K. (1998). About 23 million documents match your query. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (Doctoral Consortium). (pp. 64–65). ACM Press.
Rodden, K., Basalaj, W., Sinclair, D., and Wood, K. (2001). Does organisation by similarity assist image browsing?. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 190–197). ACM Press.
Rodden, K., Fu, X., Aula, A., and Spiro, I. (2008). Eye-mouse coordination patterns on web search results pages. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 2997–3002). ACM Press.
Rotter, J.B. (1954). Social learning and clinical psychology. Englewood Cliffs, NJ, US: Prentice-Hall, Inc
Rose, D.E. and Levinson, D. (2004). Understanding user goals in web search. In Proceedings of the international conference on the World Wide Web (pp. 13–19). ACM Press.
Rosen, J. (2012). The right to be forgotten. Stanford Law Review Online, 64, 88.Google Scholar
Rosenbaum, P. and Rubin, D. (1983). The central role of propensity score in observational studies for causal effects. Biometrica, 70, 41–55.Google Scholar
Rosenholtz, R., Li, Y., and Nakano, L. (2007). Measuring visual clutter. Journal of Vision, 7(2), 1–22.Google Scholar
Rosenthal, R. (1966). Experimenter Effects in Behavioral Research. New York: Appleton-Century-Crofts464.
Ross, C. (1999). Finding without seeking: the information encounter in the context of reading for pleasure. Information Processing and Management, 35(6), 783–799.CrossRefGoogle Scholar
Rubin, M., and Badea, C. (2010). The central tendency of a social group can affect ratings of its intragroup variability in the absence of social identity concerns. Journal of Experimental Social Psychology, 46, 410–415.Google Scholar
Rubin, V.L., Burkell, J., and Quan-Haase, A. (2011). Facets of serendipity in everyday chance encounters: A grounded theory approach to blog analysis. Information Research, 16(3).Google Scholar
Ruotsalo, T., Jacucci, G., Myllymäki, P., and Kaski, S. (2015). Interactive intent modeling: Information discovery beyond search. Communications of the ACM, 58(1), 86–92.Google Scholar
Rushinek, A. and Rushinek, S.F. (1986). What makes users happy?Communications of the ACM, 29(1), 594–598.Google Scholar
Rushkoff, D. (2005). Get Back in the Box: Innovation from the Inside Out, New York: Harper Collins.
Russell, D.M. (2010). Why is search easy and hard? Understanding serendipity and expertise in search. Keynote presentation of the workshop on human-computer interaction and informational retrieval.
Russell, D.M. and Grimes, C. (2007). Assigned tasks are not the same as self-chosen Web search tasks. In Proceedings of the annual Hawaii international conference on system sciences (pp. 83–83). IEEE.
Russell, D.M.Stefik, M.J., Pirolli, P., and Card, S.K. (1993). The cost structure of sensemaking. In Proceedings of the INTERACT and SIGCHI conference on human factors in computing systems (pp. 269–276). ACM Press.
Russell-Rose, T., and Tate, T. (2012). Designing the Search Experience: The Information Architecture of Discovery. San Francisco, CA: Morgan Kaufman.
Ruthven, I. (2001). Abduction, Explanation and Relevance Feedback. Unpublished doctoral dissertation, Glasgow: University of Glasgow.
Ruthven, I. (2002). On the use of explanations as mediating device for relevance feedback. In Proceedings of the European conference on digital libraries (pp. 338–345).
Ruthven, I. (2003). Re-examining the potential effectiveness of interactive query expansion. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 213–220). ACM Press.
Ruthven, I. (2008). Interactive information retrieval. Annual review of information science and technology, 42(1), 43–91.Google Scholar
Ruthven, I. and Kelly, D. (2012). Interactive Information-Seeking Behaviour and Retrieval. London: Facet Publishing.
Ruthven, I., Lalmas, M., and Van Rijsbergen, C.J. (2002). Ranking expansion terms using partial and ostensive relevance. In Proceedings of the COLIS conference on conceptions of library and information science (pp. 199–219).Google Scholar
Rzeszotarski, J.M. and Morris, M.R. (2014). Estimating the social costs of friendsourcing. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2735–2744). ACM Press.
Sackett, D.L. (1979). Bias in analytic research. Journal of Chronic Diseases, 32(1–2), 5–63.Google Scholar
Sadilek, A., Brennan, S., Kautz, H., and Silenzio, V. (2013). nEmesis: Which restaurants should you avoid today?. In Proceedings of the AAAI conference on human computation and crowd sourcing.
Sadilek, A. and Krumm, J. (2012). Far out: Predicting long-term human mobility. In Proceedings of the AAAI conference on artificial intelligence (pp. 814–820). AAAI Press.Google Scholar
Sadilek, A., Kautz, H.A., and Silenzio, V. (2012). Predicting disease transmission from geo-tagged micro-blog data. In Proceedings of the AAAI conference on artificial intelligence (pp. 136–142). AAAI Press.
Sahib, N.G., Tombros, A., and Ruthven, I. (2010). Enabling interactive query expansion through eliciting the potential effect of expansion terms. In Proceedings of the European conference on information retrieval (pp. 532–543). Springer Berlin Heidelberg.
Saito, H. and Miwa, K. (2001). A cognitive study of information seeking processes in the WWW: Effects of searcher's knowledge and experience. In Proceedings of the conference Web information systems engineering (pp. 321–327). IEEE.
Sakai, T. (2014). Statistical reform in information retrieval?SIGIR Forum, 48(1), 3–12.Google Scholar
Säljö, R. (1979). Learning in the learner's perspective. I. Some common-sense conceptions, Reports from the Institute of Education, University of Gothenburg, 76.
Salojärvi, J., Puolamäki, K., and Kaski, S. (2005). Implicit relevance feedback from eye movements. In Proceedings of artificial neural networks: biological inspirations (pp. 513–518). SpringerBerlin Heidelberg.
Salton, G. (1971). The SMART Retrieval System: Experiments in Automatic Document Processing. Englewood Cliffs, NJ: Prentice-Hall.
Salton, G., Allan, J., and Buckley, C. (1993). Approaches to passage retrieval in full text retrieval systems. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 49–58). ACM Press.
Salton, G. and Buckley, C. (1990). Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41(4), 288–297.Google Scholar
Salton, G., Wong, A., and Yang, C.S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613–620.Google Scholar
San Agustin, J., Skovsgaard, H., Mollenbach, E., Barret, M., Tall, M., Hansen, D.W., and Hansen, J.P. (2010). Evaluation of a low-cost open-source gaze tracker. In Proceedings of the symposium on eye-tracking research and applications (pp. 77–80). ACM Press.
Sanderson, M. (2010). Test Collection Based Evaluation of Information Retrieval Systems. Foundations and Trends in Information Retrieval. Now Publishers Inc.
Sanderson, M. and Dumais, S. (2007). Examining repetition in user search behavior. In Proceedings of the European conference on information retrieval (pp. 597–604). SpringerBerlin Heidelberg.
Sanderson, M., Paramita, M.L., Clough, P., and Kanoulas, E. (2010). Do user preferences and evaluation measures line up?. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 555–562). ACM Press.
Sandstrom, P.E. (1994). An optimal foraging approach to information seeking and use. Library Quarterly, 64, 414–449.Google Scholar
Sankar, A. and Seitz, S. (2012). Capturing indoor scenes with smartphones. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 403–412). ACM Press.
Santos, R.L., Macdonald, C., and Ounis, I. (2010). Exploiting query reformulations for web search result diversification. In Proceedings of the international conference on the World Wide Web (pp. 881–890). ACM Press.
Santos, R. L., Macdonald, C., and Ounis, I. (2011). Intent-aware search result diversification. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 595–604). ACM Press.
Saracevic, T. (1975). Relevance: A review of and framework for the thinking on the notion in information science. Journal of the American Society for Information Science, 26(6), 321–343.Google Scholar
Saracevic, T. (1991). Individual differences in organizing, searching and retrieving information. In Proceedings of the annual meeting of the American society for information science (Vol. 28, pp. 82–86).Google Scholar
Saracevic, T. (1996). Relevance reconsidered. In Proceedings of the COLIS conference on conceptions of library and information science (pp. 201–218). ACM Press.
Saracevic, T. (1997). The stratified model of information retrieval interaction: Extension and applications. In Proceedings of the annual meeting for the American society for information science (Vol. 34, pp. 313–327).Google Scholar
Saracevic, T. (2007). Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance. Journal of the American Society for Information Science and Technology, 58(13), 2126–2144.Google Scholar
Saracevic, T., and Kantor, P. (1988). A study of information seeking and retrieving. III. Searchers, searches, and overlap. Journal of the American Society for Information Science, 39(3), 197–216.Google Scholar
Satyanarayanan, M. (2001). Pervasive computing: Vision and challenges. IEEE Personal Communications, 8(4), 10–17.Google Scholar
Sauro, J. and Dumas, J.S. (2009). Comparison of three one-question, post-task usability questionnaires. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1599–1608). ACM Press.
Savenkov, D., Lagun, D., and Liu, Q. (2013). Search engine switching detection based on user personal preferences and behavior patterns. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 33–42). ACM Press.
Savenkov, D. and Agichtein, E. (2014). To hint or not: Exploring the effectiveness of search hints for complex informational tasks. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1115–1118). ACM Press.
Savolainen, R. (1995). Everyday life information seeking: approaching information seeking in the context of “way of life”. Library and Information Science Research, 17(3), 259–294.Google Scholar
Savolainen, R. (2006). Time as a context of information seeking. Library and Information Science Research, 28(1), 110–127.Google Scholar
Sazoglu, F.B., Cambazoglu, B.B., Ozcan, R., Altingovde, I.S., and Ulusoy, Ö. (2013). A financial cost metric for result caching. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 873–876). ACM Press.
Scaria, A. T., Philip, R. M., West, R., and Leskovec, J. (2014). The last click: Why users give up information network navigation. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 213–222). ACM Press.
Schamber, L. (1994). Relevance and information behavior. Annual Review of Information Science and Technology, 29(1), 3–48.Google Scholar
Schamber, L., Eisenberg, M.B., and Nilan, M.S. (1990). A re-examination of relevance: toward a dynamic, situational definition. Information Processing and Management, 26(6), 755–776.Google Scholar
Schein, A.I., Popescul, A., Ungar, L.H., and Pennock, D.M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 253–260). ACM Press.
Scheirer, J., Fernandez, R., Klein, J., and Picard, R.W. (2002). Frustrating the user on purpose: a step toward building an affective computer. Interacting with Computers, 14(2), 93–118.Google Scholar
Schmeck, R.R. (1988). Learning Strategies and lLearning Styles (Perspectives on Individual Differences). New York: Plenum Press
Schmeck, R.R. and Geisler-Brenstein, E. (1989). Individual differences that affect the way students approach learning. Learning and Individual Differences, 1, 85–124.Google Scholar
Scholer, F., Kelly, D., Wu, W.-C., Lee, H.S. and Webber, W. (2013). The effect of threshold priming and need for cognition on relevance calibration and assessment. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 623–632). ACM Press.
Scholer, F., Williams, H., and Turpin, A. (2004). Query association surrogates for web search. Journal of the American Society on Informatin Science and Technology, 55(7), 637–650.Google Scholar
Schraefel, M.C. (2009). Building knowledge: what's beyond keyword search?IEEE Computer, 42(3), 52–59.Google Scholar
Schraefel, M.C., Smith, D.A., Owens, A., Russell, A., Harris, C., and Wilson, M.L. (2005). The evolving mSpace platform: Leveraging the semantic web on the trail of the Memex. In Proceedings of the ACM conference on hypertext and hypermedia (pp. 174–183). ACM Press.
Schulz, L.E., and Bonawitz, E.B. (2007). Serious fun: Preschoolers engage in more exploratory play when evidence is confounded. Developmental Psychology, 43(4), 1045.Google Scholar
Schunk, D. (2004). Learning Theories: An Educational Perspective (4th ed.). Upper Saddle River, NJ: Pearson.
Schwarz, J. and Morris, M. (2011). Augmenting web pages and search results to support credibility assessment. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1245–1254). ACM Press.
Schwartz, K.L., Roe, T., Northrup, J., Meza, J., Seifeldin, R., and Neale, A.V. (2006). Family medicine patients’ use of the internet for health information: A metronet study. The Journal of the American Board of Family Medicine, 19(1), 39–45.Google Scholar
American, Scientific. (2008). How It Works: Multitouch Surfaces Explained. http://www.scientificamerican.com/article/how-it-works-touch-surfaces-explained/ Retrieved January 9, 2010.
Sears, A., Plaisant, C., Shneiderman, B. (1992). A new era for high-precision touchscreens. In Hartson, R., and Hix, D. (Eds.), Advances in Human-Computer Interaction, Vol. 3, Ablex (pp. 1–33).
Seligman, M.E. and Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55(1), 5. American Psychological Association.
Sellen, A.J., Murphy, R., and Shaw, K.L. (2002). How knowledge workers use the web. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 227–234). ACM Press.
Sellen, A.J. and Whittaker, S. (2010). Beyond total capture: A constructive critique of lifelogging. Communications of the ACM, 53(5), 70–77.Google Scholar
Serdyukov, P., Taylor, M., Vinay, V., Richardson, M., and White, R.W. (2011). Automatic people tagging for expertise profiling in the enterprise. In Proceedings of the European conference on information retrieval (pp. 399–410). Springer Berlin Heidelberg.
Sewell, W. and Komogortsev, O. (2010). Real-time eye gaze tracking with an unmodified commodity webcam employing a neural network. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 3739–3744). ACM Press.
Shafer, G. (1976). A Mathematical Theory of Evidence (Vol. 1). Princeton: Princeton university press.
Shah, C. (2010a). Collaborative information seeking: A literature review. Advances in librarianship, 32, 3–33.Google Scholar
Shah, C. (2010b). Coagmento: A collaborative information seeking, synthesis and sense-making framework. In Proceedings of the ACM CSCW conference on computer supported cooperative work: demonstrations (pp. 6–11).
Shah, C. (2014). Collaborative information seeking. Journal of the Association for Information Science and Technology, 65(2), 215–236.Google Scholar
Shah, C. and González-Ibáñez, R. (2011). Evaluating the synergic effect of collaboration in information seeking. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 913–922). ACM Press.
Shah, C., and Pomerantz, J. (2010). Evaluating and predicting answer quality in community QA. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 411–418). ACM Press.
Shah, C. and Marchionini, G. (2010). Awareness in collaborative information seeking. Journal of the American Society for Information Science and Technology, 61(10), 1970–1986.Google Scholar
Shah, C. (2013). Effects of Awareness on Coordination in Collaborative Information Seeking. Journal of the American Society for Information Science and Technology, 64(6), 1122–1143.Google Scholar
Shahaf, D., Guestrin, C., and Horvitz, E. (2012). Trains of thought: Generating information maps. In Proceedings of the international conference on the World Wide Web (pp. 899–908). ACM Press.
Shami, N.S., Ehrlich, K., and Millen, D.R. (2008). Pick me!: Link selection in expertise search results. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1089–1092). ACM Press.
Shapira, B., Kantor, P.B., and Melamed, B. (2001). The effect of extrinsic motivation on user behavior in a collaborative information finding system. Journal of the American Society for Information Science and Technology, 52(11), 879–887.Google Scholar
Shardanand, U. and Maes, P. (1995). Social information filtering: Algorithms for automating “word of mouth”. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 210–217). ACM Press/Addison-Wesley Publishing Co.
Sharma, A. and Cosley, D. (2013). Do social explanations work?: Studying and modeling the effects of social explanations in recommender systems. In Proceedings of the international conference on the World Wide Web (pp. 1133–1144). ACM Press.
Sheldon, D., Shokouhi, M., Szummer, M., and Craswell, N. (2011). LambdaMerge: Merging the results of query reformulations. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 795–804). ACM Press.
Shen, X., Tan, B., and Zhai, C. (2005a). Context-sensitive information retrieval using implicit feedback. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 43–50).
Shen, X., Tan, B., and Zhai, C. (2005b). Implicit user modeling for personalized search. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 824–831). ACM Press.
Sherman, C. (2005). A new F-word for Google search results. Search Engine Watch. Retrieved March 8, 2005 from http://searchenginewatch.com/showPage.html?”3488076.
Sheth, B. and Maes, P. (1993). Evolving agents for personalized information filtering. In Proceedings of the IEEE conference on artificial intelligence for applications (pp. 345–352). IEEE.
Shipman, F.M., Furuta, R., Brenner, D., Chung, C.C., and Hsieh, H.W. (2000). Guided paths through Web-based collections: Design, experiences, and adaptations. Journal of the American Society for Information Science, 51(3), 260–272.Google Scholar
Shneiderman, B. (1984). Response time and display rate in human performance with computers. Computing Surveys, 16(3), 265–285.Google Scholar
Shneiderman, B. (1990). Human values and the future of technology: A declaration of empowerment. ACM SIGCAS Computers and Society, 20(3), 1–6.Google Scholar
Shneiderman, B. (1991). Touch screens now offer compelling uses. IEEE Software, 8(2), 93–94.Google Scholar
Shneiderman, B. (1992). Tree visualization with tree-maps: 2-d space-filling approach. ACM Transactions on Graphics, 11(1), 92–99.Google Scholar
Shneiderman, B. (1994). Dynamic queries for visual information seeking. IEEE Software, 11(6), 70–77.Google Scholar
Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations, In Proceedings of the IEEE symposium on visual languages (pp. 336–343). IEEE.
Shneiderman, B. (1998). Designing the User Interface: Strategies for Effective Human-Computer Interaction (3rd ed.). Reading, MA: Addison-Wesley.
Shneiderman, B. (2000). Creating creativity: user interfaces for supporting innovation. ACM Transactions on Computer-Human Interaction, 7(1), 114–138.Google Scholar
Shneiderman, B. (2002). Creativity support tools. Communications of the ACM, 45(10), 116–120.Google Scholar
Shneiderman, B. (2007). Creativity support tools: Accelerating discovery and innovation. Communications of the ACM, 50(12), 20–32.Google Scholar
Shneiderman, B. and Maes, P. (1997). Direct manipulation vs. interface agents. Interactions, 4(6), 42–61.Google Scholar
Shneiderman, B., Byrd, D. and Croft, W.B. (1998). Sorting out searching: A user-interface framework for text searches. Communications of the ACM, 41(4), 95–98.Google Scholar
Shneiderman, B., Plaisant, C., and Hesse, B. (2013). Improving health and healthcare with interactive visualization tools. IEEE Computer, 46(5), 26–34.Google Scholar
Shokouhi, M. (2013). Learning to personalize query auto-completion. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 103–112). ACM Press.
Shokouhi, M. and Guo, Q. (2015). From queries to cards: re-ranking proactive card recommendations based on reactive search history. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 695–704). ACM Press.
Shokouhi, M. and Radinsky, K. (2012). Time-sensitive query auto-completion. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 601–610). ACM Press.
Shokouhi, M. and Si, L. (2011). Federated search. Foundations and Trends in Information Retrieval, 5(1), 1–102.Google Scholar
Shokouhi, M., White, R.W., Bennett, P., and Radlinski, F. (2013). Fighting search engine amnesia: Re-ranking repeated results. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 273–282). ACM Press.
Shokouhi, M., White, R.W., and Yilmaz, E. (2015). Anchoring and adjustment in relevance estimation. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 963–966). ACM Press.
Shtok, A., Kurland, O., and Carmel, D. (2009). Predicting query performance by query-drift estimation. In Proceedings of the international conference on the theory of information retrieval (pp. 305–312). SpringerBerlin Heidelberg.
Shurman, E. and Brutlag, J. (2009). Performance related changes and their user impact. Velocity. http://oreil.ly/fTmYwz. Accessed on August 15, 2015.
Shute, S.J. and Smith, P.J. (1993). Knowledge-based search tactics. Information Processing and Management, 29(1), 29–45.Google Scholar
Sibert, L.E. and Jacob, R.J. (2000). Evaluation of eye gaze interaction. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 281–288). ACM Press.
Sidney, K.D., Craig, S.D., Gholson, B., Franklin, S., Picard, R., and Graesser, A.C. (2005). Integrating affect sensors in an intelligent tutoring system. In Proceedings of affective interactions: the computer in the affective loop workshop (pp. 7–13).
Sieg, A., Mobasher, B., and Burke, R. (2007). Web search personalization with ontological user profiles. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 525–534). ACM Press.
Silverstein, C., Marais, H., Henzinger, M., and Moricz, M. (1999). Analysis of a very large web search engine query log. SIGIR Forum, 33(1), 6–12.Google Scholar
Simon, H.A. (1971). Designing organizations for an information-rich world. Computers, Communication, and the Public Interest, 37, 40–41Google Scholar
Sinervo, B. (1997). Optimal Foraging Theory: Constraints and Cognitive Processes. Behavioral Ecology. Santa Cruz, CA: University of California.
Singer, N. (2012). You've won a badge (and now we know all about you). New York Times, 4 February 2012.
Singla, A., Horvitz, E., Kamar, E., and White, R. (2014a). Stochastic privacy. In Proceedings of the AAAI conference on artificial intelligence (pp. 152–158). AAAI Press.
Singla, A., White, R.W., Hassan, A., and Horvitz, E. (2014b). Enhancing personalization via search activity attribution. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1063–1066). ACM Press.
Singla, A., White, R.W., and Huang, J. (2010). Studying trailfinding algorithms for enhanced web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 443–450). ACM Press.
Singley, K., Lai, J., Kuang, L., and Tang, J.-M. (2008). BlueReach: Harnessing synchronous chat to support expertise sharing in a large organization. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2001–2008). ACM Press.
Skinner, B.F. (1974). About Behaviorism. New York: Knopf (distributed by Random House).
Slater, M. (1999). Measuring presence: A response to the witmer and singer presence questionnaire. Presence, 8(5), 560–565.CrossRefGoogle Scholar
Smirnova, E. and Balog, K. (2011). A user-oriented model for expert finding. In Proceedings of the European conference on information retrieval (pp. 580–592). Springer-Verlag.
Smith, C.L. and Kantor, P.B. (2008). User adaptation: good results from poor systems. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 147–154). ACM Press.
Smith, S., Glenberg, A., and Bjork, R. (1978). Environmental context and human memory. Memory and Cognition, 6(4), 342–353.Google Scholar
Smucker, M.D. and Jethani, C.P. (2010). Human performance and retrieval precision revisited. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 595–602). ACM Press.
Smucker, M.D. and Clarke, C.L. (2012a). Time-based calibration of effectiveness measures. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 95–104). ACM Press.
Smucker, M.D., and Clarke, C.L. (2012b). Modeling user variance in time-biased gain. In Proceedings of the symposium on human-computer interaction and information retrieval (p. 3). ACM Press.
Soergel, D. (1999). The rise of ontologies or the reinvention of classification, Journal of the American Society for Information Science and Technology, 50(12), 1119–1120.Google Scholar
Solovey, E., Schermerhorn, P., Scheutz, M., Sassaroli, A., Fantini, S., and Jacob, R. (2012). Brainput: Enhancing interactive systems with streaming fnirs brain input. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2193–2202). ACM Press.
Song, Y., Shi, X., and Fu, X. (2013). Evaluating and predicting user engagement change with degraded search relevance. In Proceedings of international conference on the World Wide Web (pp. 1213–1224).
Song, Y., Wang, H., and He, X. (2014). Adapting deep ranknet for personalized search. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 83–92). ACM Press.
Song, Y., Zhuang, Z., Li, H., Zhao, Q., Li, J., Lee, W.C., and Giles, C.L. (2008). Real-time automatic tag recommendation. Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 515–522). ACM Press.
Sontag, D., Collins-Thompson, K., Bennett, P.N., White, R.W., Dumais, S., and Billerbeck, B. (2012). Probabilistic models for personalizing web search. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 433–442). ACM Press.
Spärck-Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21.Google Scholar
Spärck-Jones, K., Robertson, S.E., and Sanderson, M. (2007). Ambiguous requests: Implications for retrieval tests, systems, and theories. SIGIR Forum, 41(2), 8–17.Google Scholar
Spärck-Jones, K. and van Rijsbergen, C.J. (1976). Information retrieval test collections. Journal of Documentation, 32(1), 59–75.Google Scholar
Spärck-Jones, K., Walker, S., and Robertson, S.E. (2000). A probabilistic model of information retrieval: Development and comparative experiments: Part 1. Information Processing and Management, 36(6), 779–808.Google Scholar
Speicher, M. (2012). W3touch: Crowdsourced Evaluation and Adaptation of Web Interfaces for Touch. Master's thesis, ETH Zurich.
Spence, R. (2002). Rapid, serial and visual: A presentation technique with potential. Information Visualization, 1(1), 13–19.Google Scholar
Sperber, D., and Wilson, D. (1995). Relevance: Communication and cognition (2nd ed.). Oxford: Blackwell.
Speretta, M. and Gauch, S. (2005). Personalized search based on user search histories. In Proceedings of IEEE/WIC/ACM conference on Web intelligence (pp. 622–628). IEEE.
Spink, A., Goodrum, A., Robins, D., and Wu, M.M. (1996). Search intermediaries elicitations during mediated online searching. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 120–127). ACM Press.
Spink, A., Griesdorf, H., and Bateman, J. (1998). From highly relevant to not relevant: Examining different regions of relevance. Information Processing and Management, 34(5), 599–621.Google Scholar
Spink, A. and Losee, R.M. (1996). Feedback in information retrieval. Annual Review of Information Science and Technology, 31(1), 33–78.Google Scholar
Spink, A., Park, M., Jansen, B.J., and Pedersen, J. (2006). Multitasking during Web search sessions. Information Processing and Management, 42(1), 264–275.Google Scholar
Spink, A. and Saracevic, T. (1997). Interactive information retrieval: Sources and effectiveness of search terms during mediated online searching. Journal of the American Society for Information Science, 48(8), 741–761.Google Scholar
Spivey, M.J., Grosjean, M., and Knoblich, G. (2005). Continuous attraction toward phonological competitors. Proceedings of the National Academy of Sciences of the United States of America, 102(29), 10393–10398.Google Scholar
Spoerri, A. (1993). InfoCrystal: A visual tool for information retrieval and management. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 11–20). ACM Press.
Sriram, S., Shen, X. and Zhai, C. (2004). A session-based search engine. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 492–493). ACM Press.
Stanton, I., Ieong, S., and Mishra, N. (2014). Circumlocution in diagnostic medical queries. In Proceedings of the ACM SIGIR conference on Research and development in information retrieval (pp. 133–142). ACM Press.
Stephens, D.W. and Krebs, J. R. (1986). Foraging Theory. Princeton, NJ: Princeton University Press.
Sternberg, R.J. (1999). Handbook of Creativity. Cambridge: Cambridge University Press.
Sternberg, R.J. and Lubart, T.I. (1999). The concept of creativity: Prospects and paradigms. In Sternberg, R.J. (Ed.), Handbook of Creativity (pp. 3–15). Cambridge: Cambridge University Press.
Sternberg, R.J., Kaufman, J.C., and Pretz, J.E. (2002). The Creativity Conundrum: A Propulsion Model of Kinds of Creative Contributions. New York: Psychology Press.
Stigler, G.J. (1961). The economics of information. The Journal of Political Economy, 69(3), 213–225.Google Scholar
Stoica, E. and Hearst, M. (2004). Nearly automated metadata hierarchy creation. In Proceedings of the annual conference of the North American chapter of the association for computational linguistics (Companion Volume) (pp. 117–120).
Stone, M.C., Fishkin, K., and Bier, E.A. (1994). The movable filter as a user interface tool. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 306–312). ACM Press.
Su, L.T. (1992). Evaluation measures for interactive information retrieval. Information Processing and Management, 28(4), 503–516.Google Scholar
Su, L.T. (2003). A comprehensive and systematic model of user evaluation of Web search engines. Journal of the American Society for Information Science and Technology, 54(13), 1175–1192.Google Scholar
Suchman, L.A. (1987). Plans and Situated Actions. New York: Cambridge University Press.
Sugiyama, K., Hatano, K., and Yoshikawa, M. (2004). Adaptive web search based on user profile constructed without any effort from users. In Proceedings of the international conference on the World Wide Web (pp. 675–684). ACM Press.
Sun, J.-T., Zeng, H.-J., Liu, H., Lu, Y., and Chen, Z. (2005). CubeSVD: A novel approach to personalized Web search. In Proceedings of the international conference on the World Wide Web (pp. 382–390).
Svennevig, J. (2000). Getting Acquainted in Conversation: A Study of Initial Interactions (Vol. 64). John Benjamins Publishing.
Swanson, D.R. (1988). Migraine and magnesium: Eleven neglected connections. Perspectives in biology and medicine, 31(4), 526–557.Google Scholar
Sweller, J., van Merrienboer, J., and Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.Google Scholar
Tagliacozzo, R. (1977). Estimating the satisfaction of information users. Bulletin of the Medical Library Association, 65(2), 243–249.Google Scholar
Tague-Sutcliffe, J. (1992). The pragmatics of information retrieval experimentation revisited. Information Processing and Management, 28(4), 467–490.Google Scholar
Takano, H. and Winograd, T.. (1998). Dynamic bookmarks for the WWW. In Proceedings of ACM conference on hypertext and hypermedia (pp. 297–298). ACM Press.
Talbot, J., Lee, B., Kapoor, A., and Tan, D. (2009). Ensemble matrix: Interactive visualization to support machine learning with multiple classifiers. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1283–1292). ACM Press.
Talja, S. and Hansen, P. (2006). Information sharing. In Spink, A. and Cole, C. (Eds.), New Directions in Human Information Behavior. Springer Netherlands (pp. 113–134).
Tambe, M., Scerri, P., and Pynadath, D.V. (2002). Adjustable autonomy for the real world. Journal of Artificial Intelligence Research, 17(1), 171–228.Google Scholar
Tan, B., Shen, X., and Zhai, C. (2006). Mining long-term search history to improve search accuracy. In Proceedings of the ACM SIGKDD on knowledge discovery and data mining (pp. 718–723). ACM Press.
Tan, C., Gabrilovich, E., and Pang, B. (2012). To each his own: Personalized content selection based on text comprehensibility. In Proceedings of the ACM WSDM international conference on web search and data mining (pp. 233–242). ACM Press.
Tang, J.C. (2011). Reflecting on the DARPA Red Balloon Challenge. Communications of the ACM, 54(4), 78–85.Google Scholar
Tang, R. and Solomon, P. (1998). Toward an understanding of the dynamics of relevance judgment: An analysis of one person's search behavior. Information Processing and Management, 34(2), 237–256.CrossRef
Tao, J. and Tan, T. (2005). Affective computing: A review. In Proceedings of the conference on affective computing and intelligent interaction (pp. 981–995).
Tauscher, L. and Greenberg, S. (1997). How people revisit Web pages: Empirical findings and implications for the design of history systems. International Journal of Human-Computer Studies, 47(1), 97–137.Google Scholar
Taylor, N.J., Dennis, A.R., and Cummings, J.W. (2013). Situation normality and the shape of search: The effects of time delays and information presentation on search behavior. Journal of the American Society for Information Science and Technology, 64(5), 909–928.Google Scholar
Taylor, R.S. (1968). Question-negotiation and information seeking in libraries. College and Research Libraries, 29, 178–194.Google Scholar
Taylor, S.E. (1991). Asymmetrical effects of positive and negative events: The mobilization-minimization hypothesis. Psychological Bulletin, 110(1), 67.Google Scholar
Technet. (2012). Privacy and technology in balance. http://blogs.microsoft.com/on-the-issues/2012/10/26/privacy-and-technology-in-balance/. Accessed on August 15, 2015.
Teevan, J. (2006). How people recall search result lists. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 1415–1420). ACM Press.
Teevan, J. (2007). The re: Search engine: Simultaneous support for finding and re-finding. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 23–32). ACM Press.
Teevan, J., Adar, E., Jones, R., and Potts, M.A. (2007). Information re-retrieval: Repeat queries in Yahoo's logs. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 151–158). ACM Press.
Teevan, J., Alvarado, C., Ackerman, M.S., and Karger, D.R. (2004). The perfect search engine is not enough: a study of orienteering behavior in directed search. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 415–422). ACM Press.
Teevan, J., Collins-Thompson, K., White, R.W., Dumais, S.T., and Kim, Y. (2013). Slow search: Information retrieval without time constraints. In Proceedings of the symposium on human-computer interaction and information retrieval (p. 1). ACM Press.
Teevan, J., Cutrell, E., Fisher, D., Drucker, S.M., Ramos, G., André, P., and Hu, C. (2009a). Visual snippets: Summarizing web pages for search and revisitation. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 2023–2032). ACM Press.
Teevan, J., Dumais, S.T. and Liebling, D.J. (2008). To personalize or not to personalize: modeling queries with variation in user intent. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 163–170). ACM Press.
Teevan, J., Dumais, S., and Horvitz, E. (2010). Potential for personalization. ACM Transactions on Computer-Human Interaction, 17(1), 31.Google Scholar
Teevan, J., Dumais, S.T., and Horvitz, E. (2005). Beyond the commons: Investigating the value of personalizing web search. In Proceedings of the Workshop on New Tech. for Personalized Information Access (pp. 84–92).
Teevan, J., Karlson, A., Amini, S., Brush, A.J., and Krumm, J. (2011a). Understanding the importance of location, time, and people in mobile local search behavior. In Proceedings of the international conference on human computer interaction with mobile devices and services (pp. 77–80). ACM Press.
Teevan, J., Liebling, D.J., and Ravichandran, G.G. (2011b). Understanding and predicting personal navigation. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 85–94). ACM Press.
Teevan, J., Liebling, D.J., and Lasecki, W.S. (2014a). Selfsourcing personal tasks. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 2527–2532). ACM Press.
Teevan, J., Morris, M.R., and Bush, S. (2009b). Discovering and using groups to improve personalized search. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 15–24). ACM Press.
Teevan, J., Ringel Morris, M., and Azenkot, S. (2014b). Supporting interpersonal interaction during collaborative mobile search. IEEE Computer, 47(3), 54–57.Google Scholar
Telang, R., Mukhopadhyay, T., and Wilcox, R. (1999). An empirical analysis of the antecedents of internet search engine choice. In Proceedings of the Workshop on Information Systems and Economics.
Thaler, R.H. and Sunstein, C.R. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. New Have, CT: Yale.
Thatcher, A. (2008). Web search strategies: The influence of Web experience and task type. Information Processing and Management, 44(3), 1308–1329.Google Scholar
Thibaut, J.W. and Kelley, H.H. (1959). The Social Psychology of Groups. New York: John Wiley and Sons, Inc.
Thomas, P. and Hawking, D. (2006). Evaluation by comparing result sets in context. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 94–101). ACM Press.
Thorisson, K.R. (1996). Communicative humanoids: A computational model of psychosocial dialogue skills (Doctoral dissertation, Massachusetts Institute of Technology).
Thorndike, E.L. and Woodworth, R.S. (1901). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8.Google Scholar
Thorne, A. (1987). The press of personality: A study of conversations between introverts and extraverts. Journal of Personality and Social Psychology, 53(4), 718.Google Scholar
Tianmiyu, M.A. and Ajiferuke, I.Y. (1988). A total relevance a document interaction effects model for the evaluation of information retrieval processes. Information Processing and Management, 24(4), 391–404.Google Scholar
Tintarev, N. and Masthoff, J. (2007). Effective explanations of recommendations: User-centered design. In Proceedings of the ACM RECSYS conference on recommender systems (pp. 153–156). ACM Press.
Tombros, A. and Sanderson, M. (1998). Advantages of query biased summaries in information retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 2–10). ACM Press.
Toms, E. (2000a). Understanding and facilitating the browsing of electronic text. International Journal of Human-Computer Studies, 52(3), 423–452.Google Scholar
Toms, E.G. (2000b). Serendipitous information retrieval. In Proceedings of the DELOS workshop: information seeking, searching and querying in digital libraries.
Totton, N. and Jacobs, M. (2001). Character and Personality Types. Philadelphia, PA: Open University Press.
Trattner, C., Helic, D., Singer, P., and Strohmaier, M. (2012). Exploring the differences and similarities between hierarchical decentralized search and human navigation in information networks. In Proceedings of the conference on knowledge management and knowledge technologies (p. 14). ACM Press.
Trigg, R.H. (1988). Guided tours and tabletops: tools for communicating in a hypertext environment. ACM Transactions on Information Systems, 6(4), 398–414.Google Scholar
Tsai, M.-J. and Tsai, C.-C. (2003). Information searching strategies in web-based science learning: The role of internet self-efficacy. Innovations in Education and Teaching International, 40(1), 43–50.Google Scholar
Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7).Google Scholar
Tukey, J.W. (1977). Exploratory Data Analysis. Reading, PA: Addison-Wesley.
Tulving, E. and Thomson, D. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80, 352–373.CrossRefGoogle Scholar
Tungare, M. and Pérez-Quiñones, M.A. (2009). Mental workload in multi-device personal information management. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 3431–3436). ACM Press.
Tunkelang, D. (2009). Faceted Search. San Rafael, CA: Morgan and Claypool.
Turkle, S. (1984). The Second Self: Computers and the Human Spirit. New York: Simon and Shuster.
Turpin, A.H. and Hersh, W. (2001). Why batch and user evaluations do not give the same results. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 225–231). ACM Press.
Turpin, A. and Hersh, W. (2002). User interface effects in past batch versus user experiments. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 431–432). ACM Press.
Turpin, A. and Scholer, F. (2006). User performance versus precision measures for simple search tasks. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 11–18).
Turpin, A., Scholer, F., Järvelin, K., Wu, M., and Culpepper, J.S. (2009). Including summaries in system evaluation. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 508–515).
Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.Google Scholar
Tversky, A. and Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039.Google Scholar
Twidale, M., and Nichols, D. (1996). Interfaces to support collaboration in information retrieval. Information Retrieval and Human Computer Interaction, 25–28.
Twidale, M.B., Nichols, D.M., and Paice, C.D. (1997). Browsing is a collaborative process. Information Processing and Management, 33(6), 761–783.Google Scholar
Tyler, J.R. and Tang, J.C. (2003). When can I expect an email response? A study of rhythms in email usage. In Proceedings of the European conference on computer supported cooperative work (pp. 239–258). Netherlands: Springer.
Tyler, S.K. and Teevan, J. (2010). Large scale query log analysis of re-finding. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 191–200). ACM Press.
Tyler, S.K., Teevan, J., Bailey, P., de la Chica, S. and Dandekar, N. (2015). Large scale log analysis of individuals’ domain preferences in web search. Microsoft Research Technical Report (MSR-TR-2015-48).
Twidale, M.B., Nichols, D.M., and Paice, C.D. (1997). Browsing is a collaborative process. Information Processing and Management, 33(6), 761–783.Google Scholar
Udsen, L. and Jørgensen, A. (2005). The aesthetic turn: Unravelling recent aesthetic approaches to human-computer interaction. Digital Creativity, 16(4), 205–216.Google Scholar
Urban, J., Jose, J.M., and van Rijsbergen, C.J. (2006) An adaptive technique for content-based image retrieval. Multimedia Tools and Applications, 31(1), 1–28.Google Scholar
Ustinovskiy, Y., Gusev, G., and Serdyukov, P. (2015). An optimization framework for weighting implicit relevance labels for personalized web search. In Proceedings of the international conference on the World Wide Web (pp. 1144–1154). ACM Press.
Ustinovskiy, Y. and Serdyukov, P. (2013). Personalization of web-search using short-term browsing context. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1979–1988). ACM Press.
Vakkari, P. (1999). Task complexity, problem structure and information actions: Integrating studies on information seeking and retrieval. Information Processing and Management, 35(6), 819–837.Google Scholar
Vallet, D. and Castells, P. (2012). Personalized diversification of search results. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 841–850). ACM Press.
Van Andel, P. (1994). Anatomy of the unsought finding. Serendipity: origin, history, domains, traditions, appearances, patterns and programmability. The British Journal for the Philosophy of Science, 45(2), 631–648.Google Scholar
Van Bergen, A. (1968). Task Interruption. Amasterdam: North Holland Publishing Co.
Van Kleek, M., Moore, B., Xu, C., and Karger, D.R. (2010). Eyebrowse: real-time web activity sharing and visualization. In Proceedings of the ACM SIGCHI extended abstracts on human factors in computing systems (pp. 3643–3648). ACM Press.
Vardi, M.Y. (2012). Will MOOCs destroy academia?. Communications of the ACM, 55(11), 5.Google Scholar
Varian, H.R. (1999). Economics and search. SIGIR Forum, 33(1), 1–5.Google Scholar
Vespignani, A. (2009). Predicting the behavior of techno-social systems. Science, 325(5939), 425.Google Scholar
Vickery, A. and Brooks, H.M. (1987). PLEXUS: The expert system for referral. Information Processing and Management, 23(2), 99–117.Google Scholar
Viégas, F.B., Wattenberg, M., van Ham, F., Kriss, J., and McKeon, M. (2007). Many eyes: A site for visualization at internet scale. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1121–1128.CrossRefGoogle Scholar
Villa, R., Cantador, I., Joho, H., and Jose, J.M. (2009). An aspectual interface for supporting complex search tasks. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 379–386). ACM Press.
Vlachos, M., Meek, C., Vagena, Z., and Gunopulos, D. (2004). Identification of similarities, periodicities and bursts for online search queries. In Proceedings of the ACM SIGMOD conference on the management of data (pp. 131–142). ACM Press.
Volokh, E. (2000). Personalization and privacy. Communications of the ACM, 43(8), 84–88.Google Scholar
Von Ahn, L. (2006). Games with a purpose. IEEE Computer, 39(6), 92–94. IEEE.Google Scholar
Von Ahn, L., Blum, M., Hopper, N.J., and Langford, J. (2003). CAPTCHA: Using hard AI problems for security. In Proceedings of the EUROCRYPT conference on advances in cryptology (pp. 294–311). Springer Berlin Heidelberg.
Von Ahn, L. and Dabbish, L. (2004). Labeling images with a computer game. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 319–326). ACM Press.
Von Ahn, L., Maurer, B., McMillen, C., Abraham, D., and Blum, M. (2008). Recaptcha: Human-based character recognition via web security measures. Science, 321(5895), 1465–1468.Google Scholar
Voorhees, E. and Harman, D. (Eds.). (2005). TREC Experiment and Evaluation in Information Retrieval. Boston, MA: MIT Press.
Voorhees, E.M. (1999). TREC-8 question answering track report. In Proceedings of the text retrieval conference (pp. 77–82).
Voorhees, E.M. (2009). I come not to bury Cranfield, but to praise it. In Proceedings of the workshop on human-computer interaction and retrieval (pp. 13–16).
Voorhees, E.M. and Harman, D.K. (Eds.). (2000). TREC-9. The ninth text retrieval conference. Washington, DC: GPO.
Vosniadou, S. and Brewer, W.F. (1987). Theories of knowledge restructuring in development. Review of educational research, 57(1), 51–67.
Vygotsky, L. (1962). Thought and Language: Cambridge, MA: MIT Press.
Vygotsky, L.S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
Wærn, A. (2004). User involvement in automatic filtering: An experimental study. User Modeling and User-Adapted Interaction, 14(2–3), 201–237.Google Scholar
Wang, C., Xie, X., Wang, L., Lu, Y., and Ma, W.Y. (2005). Web resource geographic location classification and detection. In conference companion of the international conference on the World Wide Web (pp. 1138–1139). ACM Press.
Wang, H., He, X., Chang, M.W., Song, Y., White, R.W., and Chu, W. (2013). Personalized ranking model adaptation for web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 323–332). ACM Press.
Wang, H., Lymberopoulos, D., and Liu, J. (2014a). Local business ambience characterization through mobile audio sensing. In Proceedings of the international conference on the World Wide Web (pp. 293–304).
Wang, H., Zhai, C., Liang, F., Dong, A., and Chang, Y. (2014b). User modeling in search logs via a nonparametric bayesian approach, In Proceedings on the ACM WSDM conference on web search and data mining (pp. 203–212). ACM Press.
Wang, J. and Zhu, J. (2009). Portfolio theory of information retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 115–122). ACM Press.
Wang, K., Walker, T., and Zheng, Z. (2009). PSkip: Estimating relevance ranking quality from web search clickthrough data. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1355–1364). ACM Press.
Wang, L., Wang, C., Xie, X., Forman, J., Lu, Y., Ma, W.Y., and Li, Y. (2005). Detecting dominant locations from search queries. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 424–431). ACM Press.
Wang, T., Plaisant, C., Quinn, A. J., Stanchak, R., Murphy, S., and Shneiderman, B. (2008). Aligning temporal data by sentinel events: Discovering patterns in electronic health records. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 457–466). ACM Press.
Wang, X. and Zhai, C. (2009). Beyond hyperlinks: Organizing information footprints in search logs to support effective browsing. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 1237–1246). ACM Press.
Wang, J. and Zhu, J. (2009). Portfolio theory of information retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 115–122). ACM Press.
Wang, Y., Huang, X., and White, R.W. (2013). Characterizing and supporting cross-device search tasks. In Proceedings of the ACM WSDM conference on Web search and data mining (pp. 707–716). ACM Press.
Want, R., Hopper, A., Falcao, V., and Gibbons, J. (1992). The active badge location system. ACM Transactions on Information Systems, 10(1), 91–102.Google Scholar
Wasserman, S. and Faust, K. (1994). Social network analysis in the social and behavioral sciences. In Wasserman, S. and Faust, K. (eds.), Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press (pp. 1–27).
Watanabe, Y., Okada, Y., Kim, Y.-B., and Takeda, T. (1998). Translation camera. In Proceedings of the international conference on pattern recognition (pp. 613–617).
Watts, D.J. and Strogatz, S.H. (1998). Collective dynamics of ‘small-world'networks. Nature, 393(6684), 440–442.Google Scholar
Weber, I. and Castillo, C. (2010). The demographics of web search. In Proceedings of the ACM SIGIR conference on Research and development in information retrieval (pp. 523–530). ACM Press.
Weber, S.J. and Cook, T.D. (1972). Subject effects in laboratory research: An examination of subject roles, demand characteristics, and valid inference. Psychological Bulletin, 77(4), 273.Google Scholar
Weber, I., Garimella, V.R.K., and Borra, E. (2012). Mining web query logs to analyze political issues. In Proceedings of the Annual ACM Web Science Conference (pp. 330–334). ACM Press.
Webster, J., and Ho, H. (1997). Audience engagement in multimedia presentations. The DATA BASE for Advances in Information Systems, 28(2), 63–77.CrossRefGoogle Scholar
Wedig, S. and Madani, O. (2006). A large-scale analysis of query logs for assessing personalization opportunities. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 742–747). ACM Press.Google Scholar
Weinreich, H., Obendorf, H., Herder, E., and Mayer, M. (2006). Off the beaten tracks: Exploring three aspects of web navigation. In Proceedings of the international conference on the World Wide Web (pp. 133–142). ACM Press.
Weinreich, H., Obendorf, H., Herder, E., and Mayer, M. (2008). Not quite the average: An empirical study of Web use. ACM Transactions on the Web, 2(1), 5.Google Scholar
Weiser, M. (1993). Some computer science issues in ubiquitous computing. Communications of the ACM, 36(7), 75–84.
Weisz, J.D., Erickson, T., and Kellogg, W.A. (2006). Synchronous broadcast messaging: The use of ICT. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1293–1302). ACM Press.
West, R. and Leskovec, J. (2012a). Automatic versus human navigation in information networks. In Proceedings of the international conference on weblogs and social media. AAAI Press.
West, R. and Leskovec, J. (2012b). Human wayfinding in information networks. In Proceedings of the international conference on the World Wide Web (pp. 619–628). ACM Press.
West, R., Pineau, J., and Precup, D. (2009). Wikispeedia: An online game for inferring semantic distances between concepts. In Proceedings of the international joint conference on artifical intelligence (pp. 1598–1603).
West, R., White, R.W., and Horvitz, E. (2013a). From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. In Proceedings of the international conference on the World Wide Web (pp. 1399–1410). International World Wide Web Conferences Steering Committee.
West, R., White, R.W., and Horvitz, E. (2013b). Here and there: Goals, activities, and predictions about location from geotagged queries. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 817–820). ACM Press.
Wexelblat, A. and Maes, P. (1999). Footprints: History-rich tools for information foraging. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 270–277). ACM Press.
Weybrew, B.B. (1984). The Zeigarnik phenomenon revisited: Implications for enhancement of morale. Perceptual and Motor Skills, 58(1), 223–226.CrossRefGoogle Scholar
Wheeldon, R. and Levene, M. (2003). The best trail algorithm for assisted navigation of web sites. In Proceedings of the latin American web congress (pp. 166–178). IEEE.
White, R., Ruthven, I., and Jose, J.M. (2002a). Finding relevant documents using top ranking sentences: An evaluation of two alternative schemes. In Proceedings of ACM SIGIR conference on research and development in information retrieval (pp. 57–64). ACM Press.
White, R., Ruthven, I., and Jose, J.M. (2002b). The use of implicit evidence for relevance feedback in web retrieval. In Advances in Information Retrieval (pp. 93–109). Springer Berlin Heidelberg.
White, R.W. (2004). A visualisation technique to communicate implicit feedback decisions. In Proceedings of the European conference on information retrieval (Vol. 2, pp. 23–24).Google Scholar
White, R.W. (2006). Using searcher simulations to redesign a polyrepresentative implicit feedback interface. Information Processing and Management, 42(5), 1185–1202.Google Scholar
White, R.W. (2011). Interactive techniques. In Ruthven, I. and Kelly, D. (Eds.), Interactive Information Seeking, Behaviour and Retrieval (pp. 171–188). London: Facet Publishing.
White, R.W. (2013). Beliefs and biases in Web search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 3–10). ACM Press.
White, R.W. (2014). Belief dynamics in Web search. Journal of the Association for Information Science and Technology, 65(11), 2165–2178.Google Scholar
White, R.W., Bailey, P., and Chen, L. (2009a). Predicting user interests from contextual information. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 363–370). ACM Press.
White, R.W., Bilenko, M., and Cucerzan, S. (2007). Studying the use of popular destinations to enhance web search inter-action. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 159–166). ACM Press.
White, R.W. and Buscher, G. (2012a). Text selections as implicit relevance feedback. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1151–1152). ACM Press.
White, R.W. and Buscher, G. (2012b). Characterizing local interests and local knowledge. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1607–1610). ACM.
White, R.W., Chu, W., Hassan, A., He, X., Song, Y., and Wang, H. (2013a). Enhancing personalized search by mining and modeling task behavior. In Proceedings of the international conference on the World Wide Web (pp. 1411–1420). International World Wide Web Conferences Steering Committee.
White, R.W. and Drucker, S.M. (2007). Investigating behavioral variability in web search. In Proceedings of the international conference on the World Wide Web (pp. 21–30). International World Wide Web Conferences Steering Committee.
White, R.W. and Dumais, S.T. (2009). Characterizing and predicting search engine switching behavior. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 87–96). ACM Press.
White, R.W., Dumais, S.T., and Teevan, J. (2009b). Characterizing the influence of domain expertise on web search behavior. In Proceedings of the ACM WSDM conference on web search and data mining (pp. 132–141). ACM Press.
White, R.W., Harpaz, R., Shah, N., DuMouchel, W., and Horvitz, E. (2014a). Toward enhanced pharmacovigilance using patient-generated data on the internet. Nature Clinical Pharmacology and Therapeutics, 96(2), 239–246.Google Scholar
White, R.W. and Hassan, A. (2014). Content bias in online health search. ACM Transactions on the Web, 8(4), 25.Google Scholar
White, R.W. and Hassan Awadallah, A. (2015). Personalizing search on shared devices. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 523–532). ACM Press.
White, R.W., Hassan, A., Singla, A. and Horvitz, E. (2014b). From devices to people: Attribution of search activity in multi-user settings. In Proceedings of the international conference on the World wide web (pp. 431–442). ACM Press.
White, R.W. and Horvitz, E. (2009). Cyberchondria: Studies of the escalation of medical concerns in web search. ACM Transactions on Information Systems, 27(4), 23.Google Scholar
White, R.W. and Horvitz, E. (2010). Web to World: Predicting transitions from self-diagnosis to the pursuit of local medical assistance in web search. In Proceedings of the annual symposium of the American medical informatics association (p. 882). American Medical Informatics Association.
White, R.W. and Horvitz, E. (2013a). Captions and biases in diagnostic search. ACM Transactions on the Web, 7(4): 23.Google Scholar
White, R.W. and Horvitz, E. (2013b). From web search to healthcare utilization: privacy-sensitive studies from mobile data. Journal of the American Medical Informatics Association, 20(1), 61–68.Google Scholar
White, R.W. and Horvitz, E. (2015). Belief dynamics and biases in web search. ACM Transactions on Information Systems, 33(4), 18.Google Scholar
White, R.W. and Huang, J. (2010). Assessing the scenic route: Measuring the value of search trails in web logs. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 587–594). ACM Press.
White, R.W., Jose, J.M., and Ruthven, I. (2003). A task-oriented study on the influencing effects of query-biased summarisation in web searching. Information Processing and Management, 39(5), 707–733.Google Scholar
White, R.W., Jose, J.M., and Ruthven, I. (2005a). Using top-ranking sentences to facilitate effective information access. Journal of the American Society for Information Science and Technology, 56(10), 1113–1125.Google Scholar
White, R.W., Kapoor, A., and Dumais, S.T. (2010). Modeling long-term search engine usage. In Proceedings of the conference on user modeling, adaptation, and personalization (pp. 28–39). SpringerBerlin Heidelberg.
White, R.W. and Kelly, D. (2006). A study on the effects of personalization and task information on implicit feedback performance. In Proceedings of the ACM international conference on information and knowledge management (pp. 297–306). ACM Press.
White, R.W., Kules, B., Drucker, S.M., and Schraefel, M.C. (2006a). Supporting exploratory search, Communications of the ACM, 49(4), 36–39.Google Scholar
White, R.W. and Marchionini, G. (2007). Examining the effectiveness of real-time query expansion. Information Processing and Management, 43(3), 685–704.Google Scholar
White, R. W. and Morris, D. (2007). Investigating the querying and browsing behavior of advanced search engine users. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 255–262). ACM Press.
White, R.W., Muresan, G., and Marchionini, G. (2006b). Report on the ACM SIGIR 2006 workshop on evaluating exploratory search systems. SIGIR Forum, 40(2), 52–60. ACM Press.Google Scholar
White, R.W. and Richardson, M. (2012). Effects of expertise differences in synchronous social Q&A. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1055–1056). ACM Press.
White, R.W., Richardson, M., Bilenko, M., and Heath, A.P. (2008). Enhancing web search by promoting multiple search engine use. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 43–50). ACM Press.
White, R.W., Richardson, M., and Liu, Y. (2011). Effects of community size and contact rate in synchronous social Q&A. In Proceedings of ACM SIGCHI conference on human factors in computing systems (pp. 2837–2846). ACM Press.
White, R.W. and Roth, R.A. (2009). Exploratory Search: Beyond the Query-Response Paradigm. San Rafael, CA: Morgan and Claypool.
White, R.W. and Ruthven, I. (2007). A study of interface support mechanisms for interactive information retrieval. Journal of the American Society for Information Science and Technology, 57(7), 933–948.Google Scholar
White, R.W., Ruthven, I., and Jose, J.M. (2002b). The use of implicit evidence for relevance feedback in web retrieval. In Proceedings of European colloquium on information retrieval research (pp. 93–109). Springer.
White, R.W., Ruthven, I., and Jose, J.M. (2005b). A study of factors affecting the utility of implicit relevance feedback. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 35–42). ACM Press.
White, R.W., Ruthven, I., Jose, J.M., and Van Rijsbergen, C.J. (2005c). Evaluating implicit feedback models using searcher simulations. ACM Transactions on Information Systems, 23(3), 325–361.
White, R.W. and Singla, A. (2011). Finding our way on the web: Exploring the role of waypoints in search interaction. In Proceedings of the nternational conference companion on the World Wide Web (pp. 147–148). International World Wide Web Conferences Steering Committee.
White, R.W., Tatonetti, N.P., Shah, N.H., Altman, R.B., and Horvitz, E. (2013b). Web-scale pharmacovigilance: Listening to signals from the crowd. Journal of the American Medical Informatics Association, 20(3), 404–408.Google Scholar
Whittaker, S. (1996). Talking to strangers: An evaluation of the factors affecting electronic collaboration. In Proceedings of the ACM CSCW conference on computer supported cooperative work (pp. 409–418). ACM Press.
Whittaker, S. and Sidner, C. (1996). Email overload: Exploring personal information management of email. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 276–283). ACM Press.
Wickens, C.D. (2002). Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science, 3(2), 159–177.Google Scholar
Wigdor, D., Forlines, C., Baudisch, P., Barnwell, J., and Shen, C. (2007). Lucid touch: A see-through mobile device. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 269–278). ACM Press.
Wildemuth, B.M. (2004). The effects of domain knowledge on search tactic formulation. Journal of the American Society for Information Science and Technology, 55(3), 246–258.Google Scholar
Williams, J.G., Sochats, K.M., and Morse, E. (1995). Visualization. In Williams, M.E. (Ed.), Annual Review of Information Science and Technology, 30 (pp. 161–207). Medford, NJ: ASIS/Information Today Inc.
Williams, K.D. and Karau, S.J. (1991). Social loafing and social compensation: The effects of expectation of co-worker performance. Journal of Personality and Social Psychology, 61(4), 570–581.Google Scholar
Williamson, C., and Shneiderman, B. (1992). The Dynamic HomeFinder: Evaluating dynamic queries in a real-estate information exploration system. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 338–346). ACM Press.
Williamson, K. (1998). Discovered by chance: The role of incidental information acquisition in an ecological model of information use. Library Information Science Research, 20(1), 23–40.Google Scholar
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin and Review, 9(4), 625–636.Google Scholar
Wilson, M.J. and Wilson, M.L. (2013). A comparison of techniques for measuring sensemaking and learning within participant-generated summaries. Journal of the American Society for Information Science and Technology, 64(2), 291–306.Google Scholar
Wilson, M.L. and Elsweiler, D. (2010). Casual-leisure searching: The exploratory search scenarios that break our current models. In Proceedings of the workshop on human-computer interaction and information retrieval.
Wilson, M.L., Kules, B., and Shneiderman, B. (2010). From keyword search to exploration: Designing future search interfaces for the web. Foundations and Trends in Web Science, 2(1), 1–97.Google Scholar
Wilson, T.D. (1997). Information behaviour: An interdisciplinary perspective. Information Processing and Management, 33(4), 551–572.Google Scholar
Wilson, T.D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249–270.Google Scholar
Witten, I.H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, CA: Morgan Kaufmann.
Wittenburg, K., Das, D., Hill, W., and Stead, L. (1995). Group asynchronous browsing on the World Wide Web. In Proceedings of the international conference on the World Wide Web (pp. 51–62). International World Wide Web Conferences Steering Committee.
Wittrock, M. (1974). Learning as a generative activity. Educational Psychologist, 11, 87–95.Google Scholar
Wobbrock, J.O., Forlizzi, J., Hudson, S.E., and Myers, B.A. (2002). WebThumb: Interaction techniques for small-screen browsers. In Proceedings of the ACM UIST symposium on user interface software and technology (pp. 205–208). ACM Press.
Wobbrock, J.O., Morris, M.R., and Wilson, A.D. (2009). User-defined gestures for surface computing. In Proceedings of the ACM SIGCHI Conference on human factors in computing systems (pp. 1083–1092). ACM Press.
Wobbrock, J.O., Rubinstein, J., Sawyer, M.W., and Duchowski, A.T. (2008). Longitudinal evaluation of discrete consecutive gaze gestures for text entry. In Proceedings of the symposium on eye tracking research and applications (pp. 11–18). ACM Press.
Wolf, G. (2010). The data-driven life. The New York Times, 28.Google Scholar
Wolfe, J.M. (1994). Guided search 2.0: A revised model of visual search. Psychonomic Bulletin and Review, 1(2), 202–238.Google Scholar
Wolpaw, J.R., McFarland, D.J., Neat, G.W., and Forneris, C.A. (1991). An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology, 78(3), 252–259.Google Scholar
Wongsuphasawat, K., Guerra Gómez, J.A., Plaisant, C., Wang, T.D., Taieb-Maimon, M., and Shneiderman, B. (2011). LifeFlow: Visualizing an overview of event sequences. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1747–1756). ACM Press.
Woodruff, A., Faulring, A., Rosenholtz, R., Morrsion, J., and Pirolli, P. (2001). Using thumbnails to search the Web. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 198–205). ACM Press.
Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., and Freeman, W. (2012). Eulerian video magnification for revealing subtle changes in the world. ACM Transactions on Graphics, 31(4), 65.CrossRefGoogle Scholar
Wu, S., Liu, S., Cosley, D., and Macy, M. (2011). Mining collective local knowledge from Google MyMaps. In Proceedings of the international conference companion on the World Wide Web (pp. 151–152). ACM Press.
Wu, W.C., Kelly, D., and Sud, A. (2014). Using information scent and need for cognition to understand online search behavior. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 557–566). ACM Press.
Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., and Li, H. (2010). Context-aware ranking in Web search. In Proceedings of the ACM SIGIR conference of research and development in information retrieval (pp. 451–458). ACM Press.
Xie, I. and Cool, C. (2009). Understanding help seeking within the context of searching digital libraries. Journal of American Society for Information Science and Technology, 60(3), 477–494.Google Scholar
Xiong, L. and Agichtein, E. (2007). Towards privacy-preserving query log publishing. In Proceedings of the workshop on query log analysis: social and technological challenges.
Xu, D., Liu, Y., Zhang, M., Ma, S., and Ru, L. (2012). Incorporating revisiting behaviors into click models. In Proceedings of the ACM WSDM international conference on web search and data mining (pp. 303–312). ACM Press.CrossRef
Xu, S., Jiang, H., and Lau, F.C.M. (2011). Mining user dwell time for personalized web search re-ranking. In Proceedings of the international joint conference on artificial intelligence (pp. 2367–2372).
Xu, Y. and Mease, D. (2009). Evaluating web search using task completion time. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 676–677). ACM Press.
Xu, Y., Wang, K., Zhang, B., and Chen, Z. (2007). Privacy-enhancing personalized web search. In Proceedings of the international conference on the World Wide Web (pp. 591–600). ACM Press.
Xu, Y. and Yin, H. (2008). Novelty and topicality in interactive information retrieval. Journal of the American Society for Information Science and Technology, 59(2), 201–215.Google Scholar
Yamauchi, T. (2013). Mouse trajectories and state anxiety: Feature selection with random forest. In Proceedings of the humaine association conference on affective computing and intelligent interaction (pp. 399–404). IEEE.
Yamauchi, T., Kohn, N., and Yu, N.Y. (2007). Tracking mouse movement in feature inference: Category labels are different from feature labels. Memory and Cognition, 35(5), 852–863.Google Scholar
Yan, J., Chu, W., and White, R.W. (2014). Cohort modeling for enhanced personalized search. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 505–514). ACM Press.
Yang, J., Yang, W., Denecke, M., and Waibel, A. (1999). Smart sight: A tourist assistant system. In Proceedings of the international symposium on wearable computers (pp. 73–78). IEEE.
Yankelovich, N., Levow, G.A., and Marx, M. (1995). Designing SpeechActs: Issues in speech user interfaces. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 369–376). ACM Press/Addison-Wesley Publishing Co.
Yankelovich, N., Meyrowitz, N., and van Dam, A. (1985). Reading and writing the electronic book. IEEE Computer, 18(10), 15–30.Google Scholar
Yarbus, A.L. (1967). Eye Movements and Vision. New York: Plenum Press.
Yap, K.K., Srinivasan, V., and Motani, M. (2005). MAX: Human-centric search of the physical world. In Proceedings of the international conference on embedded networked sensor systems (pp. 166–179). ACM Press.
Yee, K.P., Swearingen, K., Li, K., and Hearst, M. (2003). Faceted metadata for image search and browsing. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 401–408). ACM Press.
Yilmaz, E., Shokouhi, M., Craswell, N., and Robertson, S. (2010). Expected browsing utility for web search evaluation. In Proceedings of the ACM CIKM international conference on information and knowledge management (pp. 1561–1564). ACM Press.
Yilmaz, E., Verma, M., Craswell, N., Radlinski, F., and Bailey, P. (2014). Relevance and effort: An analysis of document utility. In Proceedings of the ACM CIKM conference on conference on information and knowledge management (pp. 91–100). ACM Press.
Yin, P., Luo, P., Lee, W.-C., and Wang, M. (2013). Silence is also evidence: Interpreting dwell time for recommendation from psychological perspective. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 989–997). ACM Press.
Yom-Tov, E., Fine, S., Carmel, D., and Darlow, A. (2005). Learning to estimate query difficulty: Including applications to missing content detection and distributed information retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 512–519). ACM Press.
Yom-Tov, E., Dumais, S., and Guo, Q. (2013). Promoting civil discourse through search engine diversity. Social Science Computer Review, 32(3), 145–154.Google Scholar
Yom-Tov, E., and Gabrilovich, E. (2013). Postmarket drug surveillance without trial costs: Discovery of adverse drug reactions through large-scale analysis of web search queries. Journal of Medical Internet Research, 15(6).Google Scholar
Yom-Tov, E., Lalmas, M., Dupret, G., Baeza-Yates, R., Donmez, P., and Lehmann, J. (2012). The effect of links on networked user engagement. In Proceedings of the international conference companion on the World Wide Web (pp. 641–642). ACM Press.
Yu, S., Yu, K., and Tresp, V. (2005). Collaborative ordinal regression. In Proceedings of the NIPS workshop on learning to rank.
Yuan, X. and White, R.W. (2012). Building the trail best traveled: Effects of domain knowledge on web search trailblazing. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 1795–1804). ACM Press.
Yue, Y., Patel, R., and Roehrig, H. (2010). Beyond position bias: Examining result attractiveness as a source of presentation bias in clickthrough data. In Proceedings of the international conference on the World Wide Web (pp. 1011–1018). ACM Press.
Zaiane, O.R. and Strilets, A. (2002). Finding similar queries to satisfy searches based on query traces. In Advances in Object-Oriented Information Systems (pp. 207–216). SpringerBerlin Heidelberg.
Zamir, O., and Etzioni, O. (1999). Grouper: A dynamic clustering interface to Web search results. Computer Networks, 31(11), 1361–1374.Google Scholar
Zelazo, P.D., Carter, A., Reznick, J., and Frye, D. (1997). Early development of executive function: A problem-solving framework. Review of General Psychology, 1(2), 198–226.CrossRefGoogle Scholar
Zellweger, P.T. (1989). Scripted documents: A hypermedia path mechanism. In Proceedings of the ACM conference on hypertext and hypermedia (pp. 1–14). ACM Press.
Zellweger, P.T., Regli, S.H., Mackinlay, J.D., and Chang, B.W. (2000). The impact of fluid documents on reading and browsing: An observational study. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 249–256). ACM Press.
Zhai, S., Morimoto, C., and Ihde, S. (1999). Manual and gaze input cascaded (MAGIC) pointing. In Proceedings of the ACM SIGCHI conference on human factors in computing systems (pp. 246–253). ACM Press.
Zhang, M., Ackerman, M.S., and Adamic, L. (2007). Expertise networks in online communities: Structure and algorithms. In Proceedings of the international conference on the World Wide Web (pp. 221–230). ACM Press.
Zhang, M., Jansen, B.J., and Spink, A. (2006). Information searching tactics of web searchers. In Proceedings of the annual meeting of the American society for information science and technology, 43(1), 1–14.
Zhang, X., Cole, M., and Belkin, N. (2011). Predicting users' domain knowledge from search behaviors. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 1225–1226). ACM Press.
Zhang, Y., Wang, D., Wang, G., Chen, W., Zhang, Z., Hu, B., and Zhang, L. (2010). Learning click models via probit bayesian inference. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 439–448). ACM Press.
Zhao, L. and Callan, J. (2010). Term necessity prediction. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 259–268). ACM Press.
Zhao, Y., Scholer, F., and Tsegay, Y. (2008). Effective pre-retrieval query performance prediction using similarity and variability evidence. In Proceedings of the European conference on information retrieval (pp. 52–64). SpringerBerlin Heidelberg.
Zhou, K., Cummins, R., Lalmas, M., and Jose, J.M. (2012). Evaluating reward and risk for vertical selection. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 2631–2634). ACM Press.
Zhou, Y. and Croft, W.B. (2006). Ranking robustness: A novel framework to predict query performance. In Proceedings of the ACM CIKM conference on information and knowledge management (pp. 567–574). ACM Press.
Zhuang, Z., Brunk, C., and Giles, C.L. (2008). Modeling and visualizing geo-sensitive queries based on user clicks. In Proceedings of the international workshop on location and the Web (pp. 73–76).
Ziegarnik, B. (1927). Uber das Behalten von erledigten und unerledigten handlungen. Psychologische Forschung, 9, 1–85.Google Scholar
Ziegler, C., McNee, S.M., Konstan, J.A., and Lausen, G. (2005). Improving recommendation lists through topic diversification. In Proceedings of the international conference on the World Wide Web (pp. 22–32). ACM Press.
Ziemkiewicz, C., Crouser, R.J., Yauilla, A.R., Su, S.L., Ribarsky, W., and Chang, R. (2011). How locus of control influences compatibility with visualization style. In Proceedings of the IEEE conference on visual analytics science and technology (pp. 81–90). IEEE.
Zimmermann, P., Guttormsen, S., Danuser, B., and Gomez, P. (2003). Affective computing: A rationale for measuring mood with mouse and keyboard. International Journal of Occupational Safety and Ergonomics, 9(4), 539–551.Google Scholar
Zimmerman, T.G., Lanier, J., Blanchard, C., Bryson, S., and Harvill, Y. (1987). A hand gesture interface device. ACM SIGCHI Bulletin, 18(4), 189–192. ACM Press.Google Scholar
Zuccon, G., Koopman, B., and Palotti, J. (2015). Diagnose this if you can. In Proceedings of the European conference on information retrieval (pp. 562–567). Springer International Publishing.
Zukerman, I. and Albrecht, D.W. (2001). Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction, 11(1–2), 5–18.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Ryen W. White
  • Book: Interactions with Search Systems
  • Online publication: 05 March 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781139525305.020
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Ryen W. White
  • Book: Interactions with Search Systems
  • Online publication: 05 March 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781139525305.020
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Ryen W. White
  • Book: Interactions with Search Systems
  • Online publication: 05 March 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781139525305.020
Available formats
×