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Evolving information filtering for personalized information service

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Abstract

Information filtering (IF) systems are important for personalized information service. However, most current IF systems suffer from low quality and long training time. In this paper, a refined evolving information filtering method is presented. This method describes user’s information need from multi-aspects and improves filtering quality through a process like natural selection. Experimental result shows this method can shorten training time, improve filtering quality, and reduce the relevance between filtering results and training sequence.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China

    Tian Fanjiang, Li Congrong & Wang Dingxing

Authors
  1. Tian Fanjiang
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  2. Li Congrong
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  3. Wang Dingxing
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Corresponding author

Correspondence to Tian Fanjiang.

Additional information

This project is supported by the National ‘863’ High-Tech Programme of China (No.863-306-ZT01-03-1), IBM China Research Lab and Huawei Enterprise Funding on Science and Technology.

TIAN Fanjiang received the Ph.D. degree from Department of Computer Science & Technology, Tsinghua University in 2000. He is currently working on information gathering and information filtering.

LI Congrong is a M.S. candidate in the Department of Computer Science & Technology, Tsinghua University. He is currently working on information filtering.

WANG Dingxing is professor in the Department of Computer Science & Technology, Tsinghua University. His research interests include parallel/distributed computing, intelligent distributed systems.

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Tian, F., Li, C. & Wang, D. Evolving information filtering for personalized information service. J. Comput. Sci. & Technol. 16, 168–175 (2001). https://doi.org/10.1007/BF02950421

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  • DOI: https://doi.org/10.1007/BF02950421

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