CONTEXT 2003: Modeling and Using Context pp 419-426 | Cite as

How to Use Enriched Browsing Context to Personalize Web Site Access

  • Cécile Bothorel
  • Karine Chevalier
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2680)

Abstract

Using a browsing context is one of the keys to web site access personalization under particular constraints. With poor user information modeling, which is a common situation, a web site cannot be adapted to the current user. Assuming the current clickstream is the only known information about a web site user (no profile, no past sessions, no identification, no content analysis of viewed pages), we propose here a method to enrich the browsing context and enhance the current user model. In a batch mode, profile-based enriched navigation patterns are computed. In on-line mode, Navire, a personal agent and its matching rule engine continually re-adapts the browsing context with pre-calculated profiles. Based on the current up-to-date context, Navire personalizes the access to a web site.

Keywords

Representing context and contextual knowledge Human-computer interaction Context and the Web 
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References

  1. [1]
    Anderson C.R., Horvitz E. (2002). Web Montage: A Dynamic Personalized Start Page, WWW2002, May 7–11, 2002, Honolulu, Hawaii, USA.Google Scholar
  2. [2]
    Balabanovic M., Shoham Y. (1997). Fab: Content-Based, Collaborative Recommendation. In Communications of the ACM, vol.40, no3, March 1997, pp 66–72.CrossRefGoogle Scholar
  3. [3]
    Borges, J., Levene, M. (1999): Data Mining of User Navigation Patterns. In Proceedings of the Workshop on Web Usage Analysis and User Profiling, 1999, pages 31–36.Google Scholar
  4. [4]
    Brézillon P. (1999) Context in Artificial Intelligence: I. A survey of the literature. Computer & Artificial Intelligence, 18(4): 321–340.MATHGoogle Scholar
  5. [5]
    Chevalier K., Corruble V., Bothorel C. (2002). SurfMiner: connaître les utilisateurs d’un site. DVP 2002, Juillet 10–11 2002, Brest, France.Google Scholar
  6. [6]
    Cooley, R., Tan, P., Srivastava, J. (1999): WebSIFT: The Web Site Information Filter System. In Proceedings of the Web Usage Analysis and User Profiling Workshop, 1999.Google Scholar
  7. [7]
    Dey, A., Abowd, G. (2000). ‘Towards a Better Understanding of Context and Context-Awareness’, Workshop on the what, who, where, when and how of context-awareness at CHI 2000, April 3rd 2000, The Hague, The Netherlands.Google Scholar
  8. [8]
    Konstan J.A., Miller B.N., Maltz D., Herlocker J.L., Gordon L.L, and Riedl J. (1997). GroupLens: Applying Collaborative Filtering to Usenet News. In Communications of the ACM, vol.40, No 3 (Mars 1997), pp. 77–87.CrossRefGoogle Scholar
  9. [9]
    Lieberman, H. (1995). Letizia: An agent that assist Web browsing. In proceedings of the International joint Conference on Artificial Intelligence in Montreal, 1995 (pp 924–929).Google Scholar
  10. [10]
    Lieberman H., Selker T. (2000). Out of context: Computers systems that adapt to, and learn from, context. IBM Systems Journal.Google Scholar
  11. [11]
    Lieberman H. (2001). Interfaces That Give and Take Advice, Chapter 21 of BOOK, p. 481. ISBN...Google Scholar
  12. [12]
    Beaudouin V., Assadi H., Beauvisage T., Lelong B., Licoppe C., Ziemalicki C., Arbues L., Lendrevie J. (2002). Parcours sur Internet: analyse des traces d’usage. Rapport RP/FTR&D/7495, France Telecom R&D, Net Value, HEC.Google Scholar
  13. [13]
    Mobasher, B., Dai, H., Luo, T., Nakagawa, S., Yuqing, S. (2000). Discovery of Aggregate Usage Profiles for Web Personalization. In Proceedings of the Web Mining for Ecommerce Workshop (WebKDD), August 2000.Google Scholar
  14. [14]
    Pennock D.M, Horvitz E. (1999). Collaborative filtering by Personality Diagnosis: a hybrid memory-and model-based approach’. IJCAI Workshop on Machine Learning for Information Filtering, International Joint Conference on Artificial Intelligence, 1999Google Scholar
  15. [15]
    Resnick P., Iacovou N., Suchak M., Bergstrom P., Riedl J. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews’. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, NC: Pages 175–186.Google Scholar
  16. [16]
    Shearin S., Lieberman H. (2001). Intelligent Profiling by Example, IUI’01, January 14–17, 2001, Santa Fe, New Mexico, USA.Google Scholar
  17. [17]
    Mamber U., Patel A., and Robison J. (2000). Experience with Personalization on YAHOO!!! In Communications of the ACM, volume 43, number 8, august 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Cécile Bothorel
    • 1
  • Karine Chevalier
    • 1
    • 2
  1. 1.France Telecom R&D, DMI/GRILannionFrance
  2. 2.LIP6Université Paris VIParisFrance

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