Earlier in the book, I discussed modeling various aspects of search behavior, such as the relevance of search results or searcher satisfaction with search systems. In this chapter, the focus turns to the process of modeling searchers’ long-term interests (via personalization) and their current search situation (via contextualization). The personalization and contextualization of the user experience are increasingly important in search and recommendation systems, and enriching queries with this information facilitates the provision of individualized and situation-dependent experiences that will be commonplace in next-generation search systems, and are increasingly evident in today's search systems.
Information about searcher's interests and intentions can be used to tailor the search experience to individual searchers and to those in similar situations. Although limited to what situational information is visible to the search engine and affected by other limitations (such as profile size and log volume), rich models of search interests and their search situations can still be developed. In this chapter, I cover both personalization and contextualization; these concepts are often conflated in the research literature, they are but they are in fact quite different. I distinguish between them primarily in terms of the nature of the data used for model construction. Specifically, I define personalization and contextualization as follows:
• Personalization: Tailored to the individual searcher. Search systems may not be representative of individual searcher's information needs (Teevan et al., 2010), and personalization can help address this issue. Methods to support personalization are usually developed by modeling the long-term activity of individual searchers (e.g., search queries and clicks over a period of thirty days or more) to truly understand their interests. It is only by monitoring search behavior longitudinally that a truly individualized user profile can be constructed. Although some authors have regarded using within-session behavior as personalization (Daoud et al., 2009; Sriram et al., 2004), the data are often too sparse (just a few queries and document selections), and too task-specific, to adequately represent the searcher so that the search experience can be tailored to them. Because they rely on short-term interaction histories, such session-based models cannot impact the first query in the session (Bennett et el., 2012). […]
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