Xuehua Shen
ABSTRACT
Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word "java" to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine.
- S. M. Beitzel, E. C. Jensen, A. Chowdhury, D. Grossman, and O. Frieder. Hourly analysis of a very large topically categorized web query log. In Proceedings of SIGIR 2004, pages 321--328, 2004.]] Google Scholar
Digital Library
- C. Clarke, N. Craswell, and I. Soboroff. Overview of the TREC 2004 terabyte track. In Proceedings of TREC 2004, 2004.]]Google Scholar
- M. Claypool, P. Le, M. Waseda, and D. Brown. Implicit interest indicators. In Proceedings of Intelligent User Interfaces 2001, pages 33--40, 2001.]] Google ScholarDigital Library
- N. Craswell, D. Hawking, R. Wilkinson, and M. Wu. Overview of the TREC 2003 web track. In Proceedings of TREC 2003, 2003.]]Google Scholar
- W. B. Croft, S. Cronen-Townsend, and V. Larvrenko. Relevance feedback and personalization: A language modeling perspective. In Proeedings of Second DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries, 2001.]]Google Scholar
- Google Personalized. http://labs.google.com/personalized.]]Google Scholar
- D. Hawking, N. Craswell, P. B. Thistlewaite, and D. Harman. Results and challenges in web search evaluation. Computer Networks, 31(11-16):1321--1330, 1999.]] Google ScholarDigital Library
- X. Huang, F. Peng, A. An, and D. Schuurmans. Dynamic web log session identification with statistical language models. Journal of the American Society for Information Science and Technology, 55(14):1290--1303, 2004.]] Google ScholarDigital Library
- G. Jeh and J. Widom. Scaling personalized web search. In Proceedings of WWW 2003, pages 271--279, 2003.]] Google ScholarDigital Library
- T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of SIGKDD 2002, pages 133--142, 2002.]] Google ScholarDigital Library
- D. Kelly and J. Teevan. Implicit feedback for inferring user preference: A bibliography. SIGIR Forum, 37(2):18--28, 2003.]] Google ScholarDigital Library
- J. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieval. In Proceedings of SIGIR'01, pages 111--119, 2001.]] Google ScholarDigital Library
- T. Lau and E. Horvitz. Patterns of search: Analyzing and modeling web query refinement. In Proceedings of the Seventh International Conference on User Modeling (UM), pages 145 --152, 1999.]] Google ScholarDigital Library
- V. Lavrenko and B. Croft. Relevance-based language models. In Proceedings of SIGIR'01, pages 120--127, 2001.]] Google ScholarDigital Library
- M. Mitra, A. Singhal, and C. Buckley. Improving automatic query expansion. In Proceedings of SIGIR 1998, pages 206--214, 1998.]] Google ScholarDigital Library
- My Yahoo! http://mysearch.yahoo.com.]]Google Scholar
- G. Nunberg. As google goes, so goes the nation. New York Times, May 2003.]]Google Scholar
- S. E. Robertson. The probability ranking principle in ir. Journal of Documentation, 33(4):294--304, 1977.]]Google ScholarCross Ref
- J. J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313--323. Prentice-Hall Inc., 1971.]]Google ScholarDigital Library
- G. Salton and C. Buckley. Improving retrieval performance by retrieval feedback. Journal of the American Society for Information Science, 41(4):288--297, 1990.]]Google ScholarCross Ref
- G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.]] Google ScholarDigital Library
- X. Shen, B. Tan, and C. Zhai. Context-sensitive information retrieval using implicit feedback. In Proceedings of SIGIR 2005, pages 43--50, 2005.]] Google ScholarDigital Library
- X. Shen and C. Zhai. Exploiting query history for document ranking in interactive information retrieval (Poster). In Proceedings of SIGIR 2003, pages 377--378, 2003.]] Google ScholarDigital Library
- A. Singhal. Modern information retrieval: A brief overview. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 24(4):35--43, 2001.]]Google Scholar
- K. Sugiyama, K. Hatano, and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In Proceedings of WWW 2004, pages 675--684, 2004.]] Google ScholarDigital Library
- E. Volokh. Personalization and privacy. Communications of the ACM, 43(8):84--88, 2000.]] Google ScholarDigital Library
- R. W. White, J. M. Jose, C. J. van Rijsbergen, and I. Ruthven. A simulated study of implicit feedback models. In Proceedings of ECIR 2004, pages 311--326, 2004.]]Google ScholarCross Ref
- J. Xu and W. B. Croft. Query expansion using local and global document analysis. In Proceedings of SIGIR 1996, pages 4--11, 1996.]] Google ScholarDigital Library
- C. Zhai and J. Lafferty. Model-based feedback in KL divergence retrieval model. In Proceedings of the CIKM 2001, pages 403--410, 2001.]] Google ScholarDigital Library
Index Terms
- Implicit user modeling for personalized search
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