TY - GEN
T1 - Implicit user modeling for personalized search
AU - Shen, Xuehua
AU - Tan, Bin
AU - Zhai, Cheng Xiang
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Implicit feedback
KW - Interactive retrieval
KW - Personalized search
KW - User model
UR - http://www.scopus.com/inward/record.url?scp=33745780139&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745780139&partnerID=8YFLogxK
U2 - 10.1145/1099554.1099747
DO - 10.1145/1099554.1099747
M3 - Conference contribution
AN - SCOPUS:33745780139
SN - 1595931406
SN - 9781595931405
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 824
EP - 831
BT - CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
T2 - CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
Y2 - 31 October 2005 through 5 November 2005
ER -