%0 Conference Paper %1 Shen:2005:IUM:1099554.1099747 %A Shen, Xuehua %A Tan, Bin %A Zhai, ChengXiang %B Proceedings of the 14th ACM International Conference on Information and Knowledge Management %C New York, NY, USA %D 2005 %I ACM %K user_interest %P 824--831 %R 10.1145/1099554.1099747 %T Implicit User Modeling for Personalized Search %U http://doi.acm.org/10.1145/1099554.1099747 %X 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. %@ 1-59593-140-6

@inproceedings{Shen:2005:IUM:1099554.1099747, 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.}, acmid = {1099747}, added-at = {2019-08-06T17:14:28.000+0200}, address = {New York, NY, USA}, author = {Shen, Xuehua and Tan, Bin and Zhai, ChengXiang}, biburl = {https://www.bibsonomy.org/bibtex/2c85901eaefd25c292fd9722ed1200238/kerndr}, booktitle = {Proceedings of the 14th ACM International Conference on Information and Knowledge Management}, description = {Implicit user modeling for personalized search}, doi = {10.1145/1099554.1099747}, interhash = {093c2edf752953b540d074892ca75013}, intrahash = {c85901eaefd25c292fd9722ed1200238}, isbn = {1-59593-140-6}, keywords = {user_interest}, location = {Bremen, Germany}, numpages = {8}, pages = {824--831}, publisher = {ACM}, series = {CIKM '05}, timestamp = {2019-08-06T17:14:28.000+0200}, title = {Implicit User Modeling for Personalized Search}, url = {http://doi.acm.org/10.1145/1099554.1099747}, year = 2005 }