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Published online by Cambridge University Press:  05 February 2016

Deepak K. Agarwal
Affiliation:
LinkedIn Corporation, California
Bee-Chung Chen
Affiliation:
LinkedIn Corporation, California
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Adomavicius, G., and Tuzhilin, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 734–49.CrossRefGoogle Scholar
Adomavicius, Gediminas, Manouselis, Nikos, and Kwon, YoungOk. 2011. Multicriteria recommender systems. Pages 769–803 of Recommender Systems Handbook. Springer.Google Scholar
Agarwal, D., and Chen, B.-C. 2009. Regression-based latent factor models. Pages 19–28 of Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09).
Agarwal, D., Chen, B.-C., Elango, P., Motgi, N., Park, S.-T., Ramakrishnan, R., Roy, S., and Zachariah, J. 2008. Online models for content optimization. Pages 17–24 of Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS'08).
Agarwal, D., Chen, B.-C., and Elango, P. 2009. Spatio-temporal models for estimating click-through rate. Pages 21–30 of Proceedings of the 18th International Conference on World Wide Web (WWW'09).
Agarwal, D., Chen, B.-C., Elango, P., and Wang, X. 2011a. Click shaping to optimize multiple objectives. Pages 132–40 of Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11).
Agarwal, Deepak, Chen, Bee-Chung, and Pang, Bo. 2011b. Personalized recommendation of user comments via factor models. Pages 571–82 of Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
Agarwal, Deepak, Chen, Bee-Chung, Elango, Pradheep, and Wang, Xuanhui. 2012. Personalized click shaping through Lagrangian duality for online recommendation. Pages 485–94 of Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Agarwal, D., Chen, B.-C., Elango, P., and Ramakrishnan, R. 2013. Content recommendation on web portals. Communications of the ACM, 56, 92–101.CrossRefGoogle Scholar
Anderson, Theodore Wilbur. 1951. Estimating linear restrictions on regression coefficients for multivariate normal distributions. Annals of Mathematical Statistics, 22(3), 327–51.CrossRefGoogle Scholar
Auer, P. 2002. Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, 3, 397–422.Google Scholar
Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E. 1995. Gambling in a rigged casino: The adversarial multi-armed bandit problem. Pages 322–31 of Proceedings of the 36th Annual Symposium on Foundations of Computer Science (FOCS'95).
Auer, P., Cesa-Bianchi, N., and Fischer, P. 2002. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47, 235–56.CrossRef
Balabanović, Marko, and Shoham, Yoav. 1997. Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66–72.CrossRefGoogle Scholar
Bell, Robert M., and Koren, Yehuda. 2007. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. Pages 43–52Data Mining of Proceedings of the 7th IEEE International Conference on Data Mining (ICDM'07).
Bell, R., Koren, Y., and Volinsky, C. 2007. Modeling relationships at multiple scales to improve accuracy of large recommender systems. Pages 95–104 of Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07).
Bengio, Yoshua,Ducharme, Réjean,Vincent, Pascal, and Janvin, Christian. 2003.Aneural probabilistic language model. Journal of Machine Learning Research, 3(Mar.), 1137–55.Google Scholar
Besag, Julian. 1986. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B (Methodological), 48(3), 259–302.Google Scholar
Bingham, Ella, and Mannila, Heikki. 2001. Random projection in dimensionality reduction: applications to image and text data. Pages 245–50 of Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Mining (KDD'01).
Blei, David, and McAuliffe, Jon. 2008. Supervised topic models. Pages 121–28 of Platt, J. C., Koller, D., Singer, Y., and Roweis, S. (eds), Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press.Google Scholar
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Mar.), 993–1022.Google Scholar
Booth, James G., and Hobert, James P. 1999. Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(1), 265–85.Google Scholar
Bottou, Léon. 2010. Large-scale machine learning with stochastic gradient descent. Pages 177–87 of Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT'2010). Springer.Google Scholar
Boyd, Stephen Poythress, and Vandenberghe, Lieven. 2004. Convex Optimization. Cambridge University Press.CrossRefGoogle Scholar
Celeux, G., and Govaert, G. 1992. A classification EMalgorithm for clustering and two stochastic versions. Computational Statistics and Data Analysis, 14, 315–32.CrossRefGoogle Scholar
Charkrabarty, Deepay, Chu, Wei, Smola, Alex, and Weimer, Markus. From Collaborative Filtering to Multitask Learning. Tech. rept.
Chen, Ye, Pavlov, Dmitry, and Canny, John F. 2009. Large-scale behavioral targeting. Pages 209–18 of Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09).
Chen, Ye, Berkhin, Pavel, Anderson, Bo, and Devanur, Nikhil R. 2011. Real-time bidding algorithms for performance-based display ad allocation. Pages 1307–15 of Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09).
Claypool, Mark, Gokhale, Anuja, Miranda, Tim, Murnikov, Pavel, Netes, Dmitry, and Sartin, Matthew. 1999. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR workshop on recommender systems, vol. 60. ACM.Google Scholar
Das, A. S., Datar, M., Garg, A., and Rajaram, S. 2007. Google news personalization: scalable online collaborative filtering. Pages 271–80 of Proceedings of the 16th International Conference on World Wide Web (WWW'07).
Datta, Ritendra, Joshi, Dhiraj, Li, Jia, and Wang, James Z. 2008. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR), 40(2), 5.CrossRefGoogle Scholar
DeGroot, M. H. 2004. Optimal Statistical Decisions. John Wiley.CrossRefGoogle Scholar
Dempster, Arthur P., Laird, Nan M., and Rubin, Donald B. 1977. Maximum likelihood from incomplete data via the EMalgorithm. Journal of the Royal Statistical Society, Series B (Methodological), 39(1), 1–38.Google Scholar
Deselaers, Thomas, Keysers, Daniel, and Ney, Hermann. 2008. Features for image retrieval: an experimental comparison. Information Retrieval, 11(2), 77–107.CrossRefGoogle Scholar
Desrosiers, C., and Karypis, G. 2011. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook, 107–44.Google Scholar
Duchi, John, Hazan, Elad, and Singer, Yoram. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12, 2121–59.Google Scholar
Efron, Brad, and Tibshirani, Rob. 1993. An Introduction to the Bootstrap. Chapman and Hall/CRC.
Fain, Daniel C., and Pedersen, Jan O. 2006. Sponsored search: A brief history. Bulletin of the American Society for Information Science and Technology, 32(2), 12–13.CrossRefGoogle Scholar
Fan, Rong-En, Chang, Kai-Wei, Hsieh, Cho-Jui, Wang, Xiang-Rui, and Lin, Chih-Jen. 2008. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, 1871–74.Google Scholar
Fontoura, Marcus, Josifovski, Vanja, Liu, Jinhui, Venkatesan, Srihari, Zhu, Xiangfei, and Zien, Jason. 2011. Evaluation strategies for top-k queries overmemory-resident inverted indexes. Proceedings of the VLDB Endowment, 4(12), 1213–1224.Google Scholar
Fu, Zhouyu, Lu, Guojun, Ting, Kai Ming, and Zhang, Dengsheng. 2011. A survey of audio-based music classification and annotation. IEEE Transactions on Multimedia, 13(2), 303–19.CrossRefGoogle Scholar
Fürnkranz, Johannes, and Hüllermeier, Eyke. 2003. Pairwise preference learning and ranking. Pages 145–56 of Proceedings of the 14th European Conference on Machine Learning (ECML'03).
Gelfand, Alan E. 1995. Gibbs sampling. Journal of the American Statistical Association, 452, 1300–1304.Google Scholar
Getoor, Lise, and Taskar, Ben. 2007. Introduction to Statistical Relational Learning. MIT Press.Google Scholar
Gilks, W. R. 1992. Derivative-free adaptive rejection sampling for Gibbs sampling. Bayesian Statistics, 4, 641–49.Google Scholar
Gilks, Walter R., Best, N. G., and Tan, K. K. C. 1995. Adaptive rejection metropolis sampling within Gibbs sampling. Journal of the Royal Statistical Society. Series C (Applied Statistics), 44(4), 455–72.Google Scholar
Gittins, J. C. 1979. Bandit processes and dynamic allocation indices. Journal of the Royal Statistical Society. Series B (Methodological), 41(2), 148–77.Google Scholar
Glazebrook, K. D., Ansell, P. S., Dunn, R. T., and Lumley, R. R. 2004. On the optimal allocation of service to impatient tasks. Journal of Applied Probability, 41, 51–72.CrossRefGoogle Scholar
Golub, Gene H., and Van Loan, Charles F. 2013. Matrix Computations. Vol. 4. Johns Hopkins University Press.Google Scholar
Good, Nathaniel, Schafer, J. Ben, Konstan, Joseph A., Borchers, Al, Sarwar, Badrul, Herlocker, Jon, and Riedl, John. 1999. Combining collaborative filtering with personal agents for better recommendations. Pages 439–46 of Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence (AAAI/IAAI).
Griffiths, Thomas L., and Steyvers, Mark. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5228–35.CrossRefGoogle ScholarPubMed
Guyon, Isabelle, and Elisseeff, André. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research, 3(Mar.), 1157–82.Google Scholar
Hastie, T., Tibshirani, R., and Friedman, J. 2009. The Elements of Statistical Learning. Springer.CrossRefGoogle Scholar
Hentenryck, Pascal Van, and Bent, Russell. 2006. Online Stochastic Combinatorial Optimization. MIT Press.Google Scholar
Herlocker, Jonathan L., Konstan, Joseph A., Borchers, Al, and Riedl, John. 1999. An algorithmic framework for performing collaborative filtering. Pages 230–37 of Proceedings of the 22nd annual International ACMSIGIR Conference on Research and Development in Information Retrieval (SIGIR'99).
Jaakkola, Tommi S., and Jordan, Michael I. 2000. Bayesian parameter estimation via variational methods. Statistics and Computing, 10(1), 25–37.CrossRefGoogle Scholar
Jaccard, Paul. 1901. Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin del la Société Vaudoise des Sciences Naturelles, 37, 547–79.Google Scholar
Jambor, Tamas, and Wang, Jun. 2010. Optimizing multiple objectives in collaborative filtering. Pages 55–62 of Proceedings of the fourth ACM Conference on Recommender Systems (RecSys'10).
Jannach, D., Zanker, M., Felfernig, A., and Friedrich, G. 2010. Recommender Systems: An Introduction. Cambridge University Press.CrossRefGoogle Scholar
Jin, Xin, Zhou, Yanzan, and Mobasher, Bamshad. 2005. A maximum entropy web recommendation system: Combining collaborative and content features. Pages 612–17 of Proceedings of the eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'05).
Jones, David Morian, and Gittins, John C. 1972. A dynamic allocation index for the sequential design of experiments. University of Cambridge, Department of Engineering.Google Scholar
Kakade, S. M., Shalev-Shwartz, S., and Tewari, A. 2008. Efficient bandit algorithms for online multiclass prediction. Pages 440–47 of Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML'08).
Katehakis, Michael N., and Veinott, Arthur F. 1987. The multi-armed bandit problem: Decomposition and computation. Mathematics of Operations Research, 12(2), 262–68.CrossRefGoogle Scholar
Kocsis, L., and Szepesvari, C. 2006. Bandit based Monte-Carlo planning. Pages 282–93 of Machine Learning: ECML. Lecture Notes in Computer Science. Springer.Google Scholar
Konstan, J. A., Riedl, J., Borchers, A., and Herlocker, J. L. 1998. Recommender systems: A grouplens perspective. In Proc. Recommender Systems, Papers from 1998 Workshop, Technical Report WS-98-08.
Koren, Yehuda. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. Pages 426–34 of Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08).
Koren, Y., Bell, R., and Volinsky, C. 2009. Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.CrossRefGoogle Scholar
Lai, Tze Leung, and Robbins, Herbert. 1985. Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics, 6(1), 4–22.CrossRefGoogle Scholar
Langford, J., and Zhang, T. 2007. The Epoch-Greedy algorithm for contextual multiarmed bandits. Pages 817–24 of Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems (NIPS'07).
Lawrence, Neil D., and Urtasun, Raquel. 2009. Non-linear matrix factorization with Gaussian processes. Pages 601–8 of Proceedings of the 26th annual International Conference on Machine Learning (ICML'09).
Li, L., Chu, W., Langford, J., and Schapire, R. E. 2010. A contextual-bandit approach to personalized news article recommendation. Pages 661–70 of Proceedings of the 19th International Conference on World Wide Web (WWW'10).
Li, Lihong, Chu, Wei, Langford, John, and Wang, Xuanhui. 2011. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. Pages 297–306 of Proceedings of the fourth ACM International Conference on Web Search and Data Mining (WSDM'11).
Lin, Chih-Jen, Weng, Ruby C., and Keerthi, S.Sathiya. 2008. Trust region Newton method for logistic regression. Journal of Machine Learning Research, 9, 627–50.
McCullagh, P. 1980. Regression models for ordinal data. Journal of the Royal Statistical Society, Series B (Methodological), 42(2), 109–42.Google Scholar
Mitchell, Thomas M. 1997. Machine Learning. 1st ed. McGraw-Hill.Google Scholar
Mitrović, Dalibor, Zeppelzauer, Matthias, and Breiteneder, Christian. 2010. Features for content-based audio retrieval. Advances in Computers, 78, 71–150.Google Scholar
Mnih, Andriy, and Salakhutdinov, Ruslan. 2007. Probabilistic matrix factorization. Pages 1257–64 of Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems (NIPS'07).
Montgomery, Douglas. 2012. Design and Analysis of Experiments. 8th ed. John Wiley.Google Scholar
Nadeau, David, and Sekine, Satoshi. 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1), 3–26.Google Scholar
Nelder, J. A., and Wedderburn, R. W. M. 1972. Generalized linear models. Journal of the Royal Statistical Society, Series A (General), 135, 370–84.CrossRefGoogle Scholar
Niño-Mora, José. 2007. A (2/3)n3 fast-pivoting algorithm for the Gittins index and optimal stopping of a Markov chain. INFORMS Journal on Computing, 19(4), 596–606.CrossRefGoogle Scholar
Pandey, S., Agarwal, D., Chakrabarti, D., and Josifovski, V. 2007. Bandits for taxonomies: A model-based approach. Pages 216–27 of Proceedings of the Seventh SIAM International Conference on Data Mining (SDM'07).
Park, Seung-Taek, Pennock, David, Madani, Omid, Good, Nathan, and DeCoste, Dennis. 2006. Näive filterbots for robust cold-start recommendations. Pages 699–705 of Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06).
Pilászy, István, and Tikk, Domonkos. 2009. Recommending new movies: Even a few ratings are more valuable than metadata. Pages 93–100 of Proceedings of the third ACM Conference on Recommender Systems (RecSys'09).
Pole, A., West, M., and Harrison, P. J. 1994. Applied Bayesian Forecasting and Time Series Analysis. Chapman-Hall.CrossRefGoogle Scholar
Porteous, Ian, Bart, Evgeniy, and Welling, Max. 2008. Multi-HDP: A non parametric Bayesian model for tensor factorization. Pages 1487–90 of Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI'08). Princeton University. 2010. WordNet. http://wordnet.princeton.edu.Google Scholar
Puterman, Martin L. 2009. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Vol. 414. John Wiley.Google Scholar
Rendle, Steffen, and Schmidt-Thieme, Lars. 2010. Pairwise interaction tensor factorization for personalized tag recommendation. Pages 81–90 of Proceedings of the third ACM International Conference on Web Search and Data Mining (WSDM'10).
Resnick, Paul, Iacovou, Neophytos, Suchak, Mitesh, Bergstrom, Peter, and Riedl, John. 1994. GroupLens: An open architecture for collaborative filtering of netnews. Pages 175–186 of Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (CSCW'94).
Ribeiro, Marco Tulio, Lacerda, Anisio, deMoura, , Edleno, Silva, Veloso, A., and Ziviani, N. 2013. Multi-objective Pareto-efficient approaches for recommender systems. ACM Transactions on Intelligent Systems and Technology, 9(1), 1–20.Google Scholar
Ricci, Francesco, Rokach, Lior, Shapira, Bracha, and Kantor, Paul B. (eds). 2011. Recommender Systems Handbook. Springer.CrossRefGoogle Scholar
Robbins, H. 1952. Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, 58, 527–35.CrossRefGoogle Scholar
Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M., and Gatford, M. 1995. Okapi at TREC-3. In Harman, D. K. (ed), The Third Text REtrieval Conference (TREC-3).Google Scholar
Rodriguez, Mario, Posse, Christian, and Zhang, Ethan. 2012. Multiple objective optimization in recommender systems. Pages 11–18 of Proceedings of the sixth ACM Conference on Recommender Systems (RecSys'12).
Rossi, Peter E., Allenby, Greg, and McCulloch, Rob P. 2005. Bayesian Statistics and Marketing. John Wiley.CrossRefGoogle Scholar
Salakhutdinov, Ruslan, and Mnih, Andriy. 2008. Bayesian probabilistic matrix factorization using Markov chainMonte Carlo. Pages 880–87 of Proceedings of the 25th International Conference on Machine Learning (ICML'08).
Salton, G., Wong, A., and Yang, C. S. 1975. A vector space model for automatic indexing. Communications of the ACM, 18(11), 613–20.CrossRefGoogle Scholar
Sarkar, Jyotirmoy. 1991. One-armed bandit problems with covariates. Annals of Statistics, 19(4), 1978–2002.CrossRefGoogle Scholar
Schein, Andrew I., Popescul, Alexandrin, Ungar, Lyle H., and Pennock, David M. 2002. Methods and metrics for cold-start recommendations. Pages 253–60 of Proceedings of the 25th annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'02).
Sculley, D., Malkin, Robert G., Basu, Sugato, and Bayardo, Roberto J. 2009. Predicting bounce rates in sponsored search advertisements. Pages 1325–34 of Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09).
Sebastiani, Fabrizio. 2002. Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1–47.CrossRefGoogle Scholar
Singh, Ajit P., and Gordon, Geoffrey J. 2008. Relational learning via collective matrix factorization. Pages 650–58 of Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08).
Smola, Alexander J., and Narayanamurthy, Shravan M. 2010. An architecture for parallel topic models. PVLDB, 3(1), 703–10.Google Scholar
Stern, D. H., Herbrich, R., and Graepel, T. 2009. Matchbox: Large scale online bayesian recommendations. Pages 111–20 of Proceedings of the 18th International Conference on World Wide Web (WWW'09).
Steuer, R. 1986. Multi-criteria Optimization: Theory, Computation and Application. John Wiley.Google Scholar
Svore, Krysta M., Volkovs, Maksims N., and Burges, Christopher J. C. 2011. Learning to rank with multiple objective functions. Pages 367–76 of Proceedings of the 20th International Conference on World Wide Web (WWW'11).
Thompson, William R. 1933. On the likelihood that one unknown probability exceeps another in view of the evidence of two samples. Biometrika, 25, 285–94.CrossRefGoogle Scholar
Varaiya, Pravin, Walrand, Jean, and Buyukkoc, Cagatay. 1985. Extensions of the multiarmed bandit problem: the discounted case. IEEE Transactions on Automatic Control, 30(5), 426–39.CrossRefGoogle Scholar
Vee, Erik, Vassilvitskii|Sergei, and Shanmugasundaram, Jayavel. 2010. Optimal online assignment with forecasts. Pages 109–18 of Proceedings of the 11th ACM Conference on Electronic Commerce (EC'10).
Vermorel, J., and Mohri, M. 2005. Multi-armed bandit algorithms and empirical evaluation. Pages 437–48 of Machine Learning: ECML. Lecture Notes in Computer Science. Springer.Google Scholar
Wang, Yi, Bai, Hongjie, Stanton, Matt, Chen, Wen-Yen, and Chang, Edward Y. 2009. pLDA: Parallel latent Dirichlet allocation for large-scale applications. Pages 301– 14 of Algorithmic Aspects in Information and Management. Springer.Google Scholar
West, M., and Harrison, J. 1997. Bayesian Forecasting and Dynamic Models. Springer.Google Scholar
Whittle, P. 1988. Restless bandits: Activity allocation in a changing world. Journal of Applied Probability, 25, 287–98.CrossRefGoogle Scholar
Yu, Kai, Lafferty, John, Zhu, Shenghuo, and Gong, Yihong. 2009. Large-scale collaborative prediction using a nonparametric random effects model. Pages 1185–92 of Proceedings of the 26th annual International Conference on Machine Learning (ICML'09).
Zhai, Chengxiang, and Lafferty, John. 2001. A study of smoothing methods for language models applied to ad hoc information retrieval. Pages 334–42 of Proceedings of the 24th annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'01).
Zhang, Liang, Agarwal, Deepak, and Chen, Bee-Chung. 2011. Generalizing matrix factorization through flexible regression priors. Pages 13–20 of Proceedings of the fifth ACM Conference on Recommender Systems (RecSys'11).
Zhu, Ciyou, Byrd, Richard H., Lu, Peihuang, and Nocedal, Jorge. 1997. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software, 23(4), 550–560.CrossRefGoogle Scholar
Ziegler, Cai-Nicolas, McNee, Sean M., Konstan, Joseph A., and Lausen, Georg. 2005. Improving recommendation lists through topic diversification. Pages 22–32 of Proceedings of the 14th International Conference on World Wide Web (WWW'05).

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