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)


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.


Information Retrieval Personalized Search Graph-Based Model 


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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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