Using probabilistic latent semantic analysis for personalized web search

Authors:
Chenxi Lin;Gui-Rong Xue;Hua-Jun Zeng;Yong Yu
Affiliations:
Apex Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Apex Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Microsoft Research Asia, Beijing, P.R. China;Apex Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China
Venue:
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Year:
2005

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Abstract

Web users use search engine to find useful information on the Internet. However current web search engines return answer to a query independent of specific user information need. Since web users with similar web behaviors tend to acquire similar information when they submit a same query, these unseen factors can be used to improve search result. In this paper we present an approach that mines these unseen factors from web logs to personalized web search. Our approach is based on probabilistic latent semantic analysis, a model based technique that is used to analyze co-occurrence data. Experimental results on real data collected by MSN search engine show the improvements over traditional web search.