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A Unified Graph-Based Iterative Reinforcement Approach to Personalized Search

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Information Retrieval Technology (AIRS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5839))

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Abstract

General information retrieval systems do not perform well in satisfying users’ individual information need. This paper proposes a novel graph-based approach based on the following three kinds of mutual reinforcement relationships: RR-Relationship (Relationship among search results), RT-Relationship (Relationship between search results and terms), TT-Relationship (Relationship among terms). Moreover, the implicit feedback information, such as query logs and immediately viewed documents, can be utilized by this graph-based model. Our approach produces better ranking results and a better query model mutually and iteratively. Then a greedy algorithm concerning the diversity of the search results is employed to select the recommended results. Based on this approach, we develop an intelligent client-side web search agent GBAIR, and web search based experiments show that the new approach can improve search accuracy over another personalized web search agent.

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Author information

Authors and Affiliations

  1. Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Yunping Huang, Le Sun & Zhe Wang

Authors
  1. Yunping Huang
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  2. Le Sun
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  3. Zhe Wang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, 790-784, Pohang, Korea

    Gary Geunbae Lee

  2. School of Computing, The Robert Gordon University, St Andrew Street, AB25 1HG, Aberdeen, UK

    Dawei Song

  3. Microsoft Reseach Asia, 5F Beijing Sigma Center, 49 Zhichun Road, Haidian District, 100190, Beijing, P.R. China

    Chin-Yew Lin

  4. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Tokyo, Japan

    Akiko Aizawa

  5. School of Literature, Shirayuri College, 1-25 Midorigaoka, Chofu-shi, 182-8525, Tokyo, Japan

    Kazuko Kuriyama

  6. Graduate School of Information Science and Technology, Hokkaido University, North 14 West 9, Kita-ku. Sapporo-shi, 060-0814, Hokkaido, Japan

    Masaharu Yoshioka

  7. Microsoft Research Asia, 5F Beijing Sigma Center, 49 Zhichun Road, Haidian District, 100190, Beijing, P.R. China

    Tetsuya Sakai

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© 2009 Springer-Verlag Berlin Heidelberg

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Huang, Y., Sun, L., Wang, Z. (2009). A Unified Graph-Based Iterative Reinforcement Approach to Personalized Search. In: Lee, G.G., et al. Information Retrieval Technology. AIRS 2009. Lecture Notes in Computer Science, vol 5839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04769-5_17

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  • DOI: https://doi.org/10.1007/978-3-642-04769-5_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04768-8

  • Online ISBN: 978-3-642-04769-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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