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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References
Anick, P.: Using terminological feedback for Web search refinement: a log-based study. In: Proceedings of WWW, pp. 89–95 (2004)
Bai, J., Nie, J., Bouchard, H., Cao, G.: Using Query Contexts in Information Retrieval. In: Proceedings of SIGIR, pp. 15–22 (2007)
Bharat, K.: SearchPad: Explicit capture of search context to support Web search. Computer Networks 33(1-6), 493–501 (2000)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of WWW (1998)
Cao, G., Nie, J., Bai, J.: Integrating word relationships into language models. In: Proceedings of SIGIR, pp. 298–305 (2005)
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)
Chirita, P., Nejdl, W., Paiu, R., Kohlschütter, C.: Using ODP metadata to personalize search. In: Proceedings of SIGIR, pp. 178–185 (2005)
Chirita, P., Firan, C., Nejdl, W.: Personalized Query Expansion for the Web. In: Proceedings of SIGIR, pp. 7–14 (2007)
Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (2002)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of SIGKDD, pp. 133–142 (2002)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)
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)
Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match text retrieval. In: Proceedings of SIGIR, pp. 272–281 (1994)
Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW 2006, pp. 727–736 (2006)
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science 41(4), 288–297 (1990)
Shen, X., Tan, B., Zhai, C.: Implicit User Modeling for Personalized Search. In: Proceedings of CIKM, pp. 824–831 (2005)
Shen, X., Tan, B., Zhai, C.: Context Sensitive Information Retrieval Using Implicit Feedback. In: Proceedings of SIGIR, pp. 43–50 (2005)
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)
Tan, B., Shen, X., Zhai, C.: Mining Long-term Search History to Improve Search Accuracy. In: SIGKDD, pp. 718–723 (2006)
Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: Proceedings of SIGIR, pp. 449–456 (2005)
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)
Zha, H.Y.: Generic summarization and key phrase extraction using mutual reinforcement principle and sentence clustering. In: Proceedings of SIGIR, pp. 113–120 (2002)
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)
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)
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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
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