Skip to main content

COBRA: a CBR-Based Aproach for Predicting Users Actions in a Web Site

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2080))

Abstract

In this paper we describe an original case-based reasoning (CBR) approach, called Cobra, that aims at predicting users requests in a web site. The basic idea underlying the Cobra approach is to model users navigational behavior in a web site by a set of cases. Typically, in a CBR system a case is composed of at least two parts: the situation or the problem part and the solution one. In the Cobra approach the situation part of a case captures a navigation experience within a user navigation session. The solution part is composed of a set of actions that may explain the transition (i.e. the move from one page to another) which follows the navigation experience described in the case situation part. The proposed case structure and the reuse phase enable to predict the access to pages that have never been visited before by any user. This is a very useful feature that matches prediction requirements in real web sites where the structure and the content change frequently over time.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Aamodt, E. Plaza. Case-Based Reasoning: Foundational Issues, Methodological Variations and Systems; AI Communications, Vol. 7:1, pp. 36–59, 1994.

    Google Scholar 

  2. R. Barrett, P. Maglo and D.C. Kellem. How to Personalise the Web. Proceedings of the International Joint Conference of Artificial Intelligence (IJCAI’97), Morgan Kaufmanns, pp. 770–775, 1997

    Google Scholar 

  3. J. Borges and M. Levene. Mining navigation patterns with hypertext probabilistic grammars. Research Note RN/99/08, Department of Computer Science, University College London, Gower Street, London, UK, February 1999.

    Google Scholar 

  4. E. Danna and A. Laroche. Auditing Web sites Using Their Access Patterns. In proceedings of the 9th International World Wide Web conference, Amsterdam, May 15-19, 2000

    Google Scholar 

  5. P. De Bra, Design Issues in Adaptive Web-Site Development, Proceedings of the Second Workshop on Adaptive Systems and User Modelling on the World Wide Web, pp. 29–39, Toronto and Banff, Canada, 1999. (Editors P. Brusilovsky and P. De Bra, available as CSN 99/07, TUE, or at http://wwwis.win.tue.nl/asum99/) 1999.

  6. P. Bursilovsky, Methods and Techniques of Adaptive Hypermedia. In User Modelling and User-Adapted Interaction 6: 87–129, Kluwer academic publishers, 1996.

    Article  Google Scholar 

  7. M. Jaczynski and B. Trousse. WWW Assisted Browsing by Reusing Past Navigations of a Group of Users. In proceedings of EWCBR’98. 1998.

    Google Scholar 

  8. R. Kanawati, M. Malek, COBRA: Une approche d’adaptation structurelle de sites Web fondée sur une technique d’apprentissage à partir des traces d’accàs utilisateurs et utilisant la méthodologie de raisonnement à partir de cas, NTIC’2000 (In french)

    Google Scholar 

  9. H. Liberman, Letizia: An Agent that Assists Web Browsing, In Proceedings of the 4th International Joint Conference on Artificial Intelligence (IJCAI’95), Morgan Kaufmann Publishers pp. 924–929, 1995

    Google Scholar 

  10. A. Mobasher, R. Cooley and J. Srivastava. Creating Adaptive Web sites Through Usage-Based Clustering of URLs. In IEEE Knowledge and Data Engineering Workshop (KDEX’99), 1999.

    Google Scholar 

  11. J. Pei, J. Han, B. Mortazavi and H. Zhu. Mining Access Patterns Eficiently from Web Logs. In proceedings of Pacific-Asia Conference on knowledge Discovery and Data Mining, pp. 396–407, 2000 (available on http://citeseer.nj.nec.com/article/pei00mining.html)

  12. M. Perkowitz and O. Etzioni, Towards Adaptive Web Sites: Conceptual Framework and Case Study, In proceedings of 8th International Conference on the World Wide Web (WWW’8), Toronto. 1999

    Google Scholar 

  13. Shahabi, A. M. Zarkesh, J. Adibi and V. Shah. Knowledge Discovery from Users Web-Page Navigation, IEEE RIDE, 1997

    Google Scholar 

  14. M. Spiliopoulou, C. Pohle and L. C. Faulstich. Improving the Effectiveness of a Web Site with Web Usage Mining. In proceedings of KDD workshop WebKDD’99, San Diego, August 1999.

    Google Scholar 

  15. B. Trousse, M. Jaczynski, and R. Kanawati. Using User Behaviour Similarity for Recommendation Computation: The Broadway Approach. In H-J Bullinger and J. Ziegler, editors, proceedings of the HCI International (HCI’99), Munich. pp 85–89. Lawrence Erlbaum Associates, august 1999

    Google Scholar 

  16. Wexelblat and P. Maes, P. Footprints: History-rich Web Browsing. In Proceedings of International Conference on Computer-Assisted Information Retrieval (RIAO’97), Montréal, pp. 75–84, 1997.

    Google Scholar 

  17. T. W. Yan, M. Jacobsen, H. Gracia-Molina and U. Dayal. From User Access Patterns to Dynamic Hypertext Linking. In proceedings of the 5th International World Wide Web Conference. Paris May 6-10, 1999, Computer Network and ISDN systems 28:1007–1014. 1999.

    Google Scholar 

  18. I. Zukerman, David W. Albrecht, Ann E. Nicholson, Predicting users’ requests on the WWW, Proceedings of the Seventh International Conference on User Modelling, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Malek, M., Kanawati, R. (2001). COBRA: a CBR-Based Aproach for Predicting Users Actions in a Web Site. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-44593-5_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42358-4

  • Online ISBN: 978-3-540-44593-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics