Detecting Session Boundaries to Personalize Search Using a Conceptual User Context

  • Mariam Daoud
  • Mohand Boughanem
  • Lynda Tamine-Lechani
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 39)

Most popular Web search engines are carachterized by “one size fits all” approaches. Involved retrieval models are based on the query-document matching without considering the user context, interests ang goals during the search. Personalized Web search tackles this problem by considering the user interests in the search process. In this chapter, we present a personalized search approach which adresses two key challenges. The first one is to model a conceptual user context across related queries using a session boundary detection. The second one is to personalize the search results using the user context. Our experimental evaluation was carried out using the TREC collection and shows that our approach is effective.


Personalized search Session Boundaries Conceptual User Context search engine 


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  1. 1.
    Bin Tan, Xuehua Shen, and ChengXiang Zhai. Mining long-term search history to improve search accuracy. In KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 718–723, New York, NY, USA, 2006. ACM.Google Scholar
  2. 2.
    Smitha Sriram, Xuehua Shen, and Chengxiang Zhai. A session-based search engine. In SIGIR'04: Proceedings of the International ACM SIGIR Conference, 2004.Google Scholar
  3. 3.
    Fang Liu, Clement Yu, and Weiyi Meng. Personalized web search for improving retrieval effectiveness. IEEE Transactions on Knowledge and Data Engineering, 16(1):28–40, 2004.CrossRefGoogle Scholar
  4. 4.
    Ahu Sieg, Bamshad Mobasher, and Robin Burke. Web search personalization with ontological user profiles. In Proceedings of the CIKM'07 conference, pages 525–534, New York, NY, USA, 2007. ACM.Google Scholar
  5. 5.
    Lynda Tamine-Lechani, Mohand Boughanem, Nesrine Zemirli. Personalized document ranking: exploiting evidence from multiple user interests for profiling and retrieval. to appear. In Journal of Digital Information Management, vol. 6, issue 5, 2008, pp. 354–366.Google Scholar
  6. 6.
    John Paul Mc Gowan. A multiple model approach to personalised information access. Master thesis in computer science, Faculty of science, Universit de College Dublin, February 2003.Google Scholar
  7. 7.
    Alessandro Micarelli and Filippo Sciarrone. Anatomy and empirical evaluation of an adaptive web-based information filtering system. User Modeling and User-Adapted Interaction, 14(2– 3):159–200, 2004.Google Scholar
  8. 8.
    Hyoung R. Kim and Philip K. Chan. Learning implicit user interest hierarchy for context in personalization. In Proceedings of IUI '03, pages 101–108, New York, NY, USA, 2003. ACM.Google Scholar
  9. 9.
    Susan Gauch, Jason Chaffee, and Alaxander Pretschner. Ontology-based personalized search and browsing. Web Intelli. and Agent Sys., 1(3–4):219–234, 2003.Google Scholar
  10. 10.
    Ahu Sieg, Bamshad Mobasher, Steve Lytinen, Robin Burke. Using concept hierarchies to enhance user queries in web-based information retrieval. In The IASTED International Conference on Artificial Intelligence and Applications. Innsbruck, Austria, 2004.Google Scholar
  11. 11.
    Mariam Daoud, Lynda Tamine-Lechani, and Mohand Boughanem. Using a concept-based user context for search personalization. to appear. In Proceedings of the 2008 International Conference of Data Mining and Knowledge Engineering (ICDMKE'08), pages 293–298. IAENG, 2008.Google Scholar

Copyright information

© Springer Science+Business Media B.V 2009

Authors and Affiliations

  • Mariam Daoud
    • 1
  • Mohand Boughanem
    • 1
  • Lynda Tamine-Lechani
    • 1
  1. 1.IRIT, University of Paul SabatierToulouseFrance

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