Feng Qiu
ABSTRACT
One hundred users, one hundred needs. As more and more topics are being discussed on the web and our vocabulary remains relatively stable, it is increasingly difficult to let the search engine know what we want. Coping with ambiguous queries has long been an important part of the research on Information Retrieval, but still remains a challenging task. Personalized search has recently got significant attention in addressing this challenge in the web search community, based on the premise that a user's general preference may help the search engine disambiguate the true intention of a query. However, studies have shown that users are reluctant to provide any explicit input on their personal preference. In this paper, we study how a search engine can learn a user's preference automatically based on her past click history and how it can use the user preference to personalize search results. Our experiments show that users' preferences can be learned accurately even from little click-history data and personalized search based on user preference yields significant improvements over the best existing ranking mechanism in the literature.
- B.J. Jansen, A. Spink, and T Saracevic. 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, 2000. Google Scholar
Digital Library
- Robert Krovetz and W. Bruce Croft. Lexical ambiguity and information retrieval. Information Systems, 10(2):115--141, 1992. Google ScholarDigital Library
- Nielsen netratings search engine ratings report. http://searchenginewatch.com/reports/article.php/2156461, 2003.Google Scholar
- J. Carroll and M. Rosson. The paradox of the active user. Interfacing Thought: Cognitive Aspects of Human-Computer Interaction, 1987. Google ScholarDigital Library
- T. Haveliwala. Topic-Sensitive PageRank. In Proceedings of the Eleventh Int'l World Wide Web Conf., 2002. Google ScholarDigital Library
- S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. In Proc. of WWW '98, 1998. Google ScholarDigital Library
- F. Qiu and J. Cho. Automatic identification of user preferences for personalized search. Technical report, UCLA Computer Science Department, 2005.Google Scholar
- J. Cho and S. Roy. Impact of Web search engines on page popularity. In Proc. of WWW '04, 2004. Google ScholarDigital Library
- Dimitri P. Bertsekas and John N. Tsitsiklis. Introduction to Probability. Athena Scientific, 2002.Google Scholar
- M. Kendall and J. Gibbons. Rank Correlation Methods. Edward Arnold, London, 1990.Google Scholar
- L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998.Google Scholar
- M. Richardson and P. Domingos. The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank. In Advances in Neural Information Processing Systems 14. MIT Press, 2002.Google Scholar
- G. Jeh and J. Widom. Scaling personalized web search. In Proceedings of the Twelfth Int'l World Wide Web Conf., 2003. Google ScholarDigital Library
- S. Kamvar, T. Haveliwala, C. Manning, and G. Golub. Exploiting the block structure of the web for computing PageRank. Technical report, Stanford University, 2003.Google Scholar
- M. Aktas, M. Nacar, and F. Menczer. Personalizing PageRank based on domain profiles. In Proc. of WebKDD 2004: KDD Workshop on Web Mining and Web Usage Analysis, 2004.Google Scholar
- F. Tanudjaja and L. Mui. Persona: A contextualized and personalized web search. In Proc. of the 35th Annual Hawaii International Conference on System Sciences, 2002. Google ScholarDigital Library
- P. Chirita, W. Nejdl, R. Paiu, and C. Kohlschuetter. Using ODP metadata to personalize search. In Proceedings of ACM SIGIR '05, 2005. Google ScholarDigital Library
- J. Sun, H. Zeng, H. Liu, Y. Lu, and Z. Chen. CubeSVD: A novel approach to personalized web search. In Proceedings of the Fourteenth Int'l World Wide Web Conf., 2005. Google ScholarDigital Library
- L. Chen and K. Sycara. Webmate: a personal agent for browsing and searching. Proc. 2nd Intl. Conf. on Autonomous Agents and Multiagent Systems, pages 132--139, 1998. Google ScholarDigital Library
- A. Pretschner and S. Gauch. Ontology based personalized search. In ICTAI, pages 391--398, 1999. Google ScholarDigital Library
- K. Sugiyama, K. Hatano, , and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In Proceedings of the Thirteenth Int'l World Wide Web Conf., 2004. Google ScholarDigital Library
Index Terms
- Automatic identification of user interest for personalized search
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