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A Unified Graph-Based Iterative Reinforcement Approach to Personalized Search

  • Yunping Huang
  • Le Sun
  • Zhe Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)

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.

Keywords

Information Retrieval Personalized Search Graph-Based Model 

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References

  1. 1.
    Anick, P.: Using terminological feedback for Web search refinement: a log-based study. In: Proceedings of WWW, pp. 89–95 (2004)Google Scholar
  2. 2.
    Bai, J., Nie, J., Bouchard, H., Cao, G.: Using Query Contexts in Information Retrieval. In: Proceedings of SIGIR, pp. 15–22 (2007)Google Scholar
  3. 3.
    Bharat, K.: SearchPad: Explicit capture of search context to support Web search. Computer Networks 33(1-6), 493–501 (2000)CrossRefGoogle Scholar
  4. 4.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of WWW (1998)Google Scholar
  5. 5.
    Cao, G., Nie, J., Bai, J.: Integrating word relationships into language models. In: Proceedings of SIGIR, pp. 298–305 (2005)Google Scholar
  6. 6.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of SIGIR, pp. 335–336 (1998)Google Scholar
  7. 7.
    Chirita, P., Nejdl, W., Paiu, R., Kohlschütter, C.: Using ODP metadata to personalize search. In: Proceedings of SIGIR, pp. 178–185 (2005)Google Scholar
  8. 8.
    Chirita, P., Firan, C., Nejdl, W.: Personalized Query Expansion for the Web. In: Proceedings of SIGIR, pp. 7–14 (2007)Google Scholar
  9. 9.
    Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (2002)Google Scholar
  10. 10.
    Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of SIGKDD, pp. 133–142 (2002)Google Scholar
  11. 11.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Lv, Y., Sun, L., et al.: An Iterative Implicit Feedback Approach to Personalized Search. In: The Proceedings of the COLING/ACL, pp. 585–592 (2006)Google Scholar
  13. 13.
    Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match text retrieval. In: Proceedings of SIGIR, pp. 272–281 (1994)Google Scholar
  14. 14.
    Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW 2006, pp. 727–736 (2006)Google Scholar
  15. 15.
    Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science 41(4), 288–297 (1990)CrossRefGoogle Scholar
  16. 16.
    Shen, X., Tan, B., Zhai, C.: Implicit User Modeling for Personalized Search. In: Proceedings of CIKM, pp. 824–831 (2005)Google Scholar
  17. 17.
    Shen, X., Tan, B., Zhai, C.: Context Sensitive Information Retrieval Using Implicit Feedback. In: Proceedings of SIGIR, pp. 43–50 (2005)Google Scholar
  18. 18.
    Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive Web search based on user profile constructed without any effort from user. In: Proceedings of WWW, pp. 675–684 (2004)Google Scholar
  19. 19.
    Tan, B., Shen, X., Zhai, C.: Mining Long-term Search History to Improve Search Accuracy. In: SIGKDD, pp. 718–723 (2006)Google Scholar
  20. 20.
    Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: Proceedings of SIGIR, pp. 449–456 (2005)Google Scholar
  21. 21.
    Wan, X., Yang, J., Xiao, J.: Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction. In: Proceedings of ACL, pp. 552–559 (2007)Google Scholar
  22. 22.
    Zha, H.Y.: Generic summarization and key phrase extraction using mutual reinforcement principle and sentence clustering. In: Proceedings of SIGIR, pp. 113–120 (2002)Google Scholar
  23. 23.
    Zhang, B., Li, H., Liu, Y., Ji, L., Xi, W., Fan, W., Chen, Z., Ma, W.Y.: Improving web search results using affinity graph. In: Proceedings of SIGIR (2005)Google Scholar
  24. 24.
    Zhou, D., Weston, J., Gretton, A., Bousquet, O., Scholkopf, B.: Ranking on data manifolds. In: Advances in Neural Information Processing Systems, pp. 169–176 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yunping Huang
    • 1
  • Le Sun
    • 1
  • Zhe Wang
    • 1
  1. 1.Institute of SoftwareChinese Academy of SciencesBeijingChina

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