University of Illinois Urbana-Champaign

Implicit User Modeling for Personalized Search

Shen, Xuehua; Tan, Bin; Zhai, ChengXiang

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Implicit User Modeling for Personalized Search.pdf

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https://hdl.handle.net/2142/11028

Description

Title
Implicit User Modeling for Personalized Search
Author(s)
  • Shen, Xuehua
  • Tan, Bin
  • Zhai, ChengXiang
Issue Date
2005-05
Keyword(s)
Database Systems Information Retrieval Web Search
Date of Ingest
2009-04-17T19:12:28Z
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 to improve retrieval accuracy. 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 a popular existing search engine.
Type of Resource
text
Genre of Resource
Technical Report
Permalink
http://hdl.handle.net/2142/11028
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