An Iterative Implicit Feedback Approach to Personalized Search

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 585–592, Sydney, July 2006. c⃝2006 Association for Computational Linguistics An Iterative Implicit Feedback Approach to Personalized Search Yuanhua Lv 1, Le Sun 2, Junlin Zhang 2, Jian-Yun Nie 3, Wan Chen 4, and Wei Zhang 2 1, 2 Institute of Software, Chinese Academy of Sciences, Beijing, 100080, China 3 University of Montreal, Canada 1 lvyuanhua@gmail.com 2 {sunle, junlin01, zhangwei04}@iscas.cn 3 nie@iro.umontreal.ca 4 chenwan@nus.edu.sg Abstract General information retrieval systems are designed to serve all users without con- sidering individual needs. In this paper, we propose a novel approach to person- alized search. It can, in a unified way, exploit and utilize implicit feedback in- formation, such as query logs and imme- diately viewed documents. Moreover, our approach can implement result re-ranking and query expansion simultaneously and collaboratively. Based on this approach, we develop a client-side personalized web search agent PAIR (Personalized Assis- tant for Information Retrieval), which supports both English and Chinese. Our experiments on TREC and HTRDP col- lections clearly show that the new ap- proach is both effective and efficient. 1 Introduction Analysis suggests that, while current information retrieval systems, e.g., web search engines, do a good job of retrieving results to satisfy the range of intents people have, they are not so well in discerning individuals’ search goals (J. Teevan et al., 2005). Search engines encounter problems such as query ambiguity and results ordered by popularity rather than relevance to the user’s in- dividual needs. To overcome the above problems, there have been many attempts to improve retrieval accuracy based on personalized information. Relevance Feedback (G. Salton and C. Buckley, 1990) is the main post-query method for automatically im- proving a system’s accuracy of a user’s individual need. The technique relies on explicit relevance assessments (i.e. indications of which documents contain relevant information). Relevance feed- back has been proved to be quite effective for improving retrieval accuracy (G. Salton and C. Buckley, 1990; J. J. Rocchio, 1971). However, searchers may be unwilling to provide relevance information through explicitly marking relevant documents (M. Beaulieu and S. Jones, 1998). Implicit Feedback, in which an IR system un- obtrusively monitors search behavior, removes the need for the searcher to explicitly indicate which documents are relevant (M. Morita and Y. Shinoda, 1994). The technique uses implicit relevance indications, although not being as ac- curate as explicit feedback, is proved can be an effective substitute for explicit feedback in in- teractive information seeking environments (R. White et al., 2002). In this paper, we utilize the immediately viewed documents, which are the clicked results in the same query, as one type of implicit feedback information. Research shows that relative preferences derived from immedi- ately viewed documents are reasonably accurate on average (T. Joachims et al., 2005). Another type of implicit feedback information that we exploit is users’ query logs. Anyone who uses search engines has accumulated lots of click through data, from which we can know what …

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