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Integrating Web Usage and Content Mining for More Effective Personalization

  • Bamshad Mobasher
  • Honghua Dai
  • Tao Luo
  • Yuqing Sun
  • Jiang Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1875)

Abstract

Recent proposals have suggested Web usage mining as an enabling mechanism to overcome the problems associated with more traditional Web personalization techniques such as collaborative or contentbased filtering. These problems include lack of scalability, reliance on subjective user ratings or static profiles, and the inability to capture a richer set of semantic relationships among objects (in content-based systems). Yet, usage-based personalization can be problematic when little usage data is available pertaining to some objects or when the site content changes regularly. For more effective personalization, both usage and content attributes of a site must be integrated into a Web mining framework and used by the recommendation engine in a uniform manner. In this paper we present such a framework, distinguishing between the offline tasks of data preparation and mining, and the online process of customizing Web pages based on a user’s active session. We describe effective techniques based on clustering to obtain a uniform representation for both site usage and site content profiles, and we show how these profiles can be used to perform real-time personalization.

Keywords

Association Rule Active Session User Session Recommendation Engine User Transaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    A. Buchner and M. D. Mulvenna. Discovering internet marketing intelligence through online analytical Web usage mining. SIGMOD Record, (4)27, 1999.Google Scholar
  2. 2.
    R. Cooley, B. Mobasher, and J. Srivastava. Web mining: Information and pattern discovery on the world wide web. In International Conference on Tools with Artificial Intelligence, pages 558–567, Newport Beach, 1997. IEEE.Google Scholar
  3. 3.
    R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining World Wide Web browsing patterns. Journal of Knowledge and Information Systems, (1)1, 1999.Google Scholar
  4. 4.
    M. S. Chen, J. S. Park, and P. S. Yu. Data mining for path traversal patterns in a Web environment. In Proceedings of 16th International Conference on Distributed Computing Systems, 1996.Google Scholar
  5. 5.
    W. B. Frakes, R. Baeza-Yates. Information Retrieval Data Structures and Algorithms. Prentice Hall, Englewood Cliffs, NJ, 1992.Google Scholar
  6. 6.
    J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 1999 Conference on Research and Development in Information Retrieval, August 1999.Google Scholar
  7. 7.
    B. 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), November 1999.Google Scholar
  8. 8.
    B. Mobasher. A Web personalization engine based on user transaction clustering. In Proceedings of the 9th Workshop on Information Technologies and Systems (WITS’99), December 1999.Google Scholar
  9. 9.
    O. Nasraoui, H. Frigui, A. Joshi, R. Krishnapuram. Mining Web access logs using relational competitive fuzzy clustering. In Proceedings of the Eight International Fuzzy Systems Association World Congress, August 1999.Google Scholar
  10. 10.
    M. O Conner, J. Herlocker. Clustering items for collaborative filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, 1999.Google Scholar
  11. 11.
    M. Perkowitz and O. Etzioni. Adaptive Web sites: automatically synthesizing Web pages. In Proceedings of Fifteenth National Conference on Artificial Intelligence, Madison, WI, 1998.Google Scholar
  12. 12.
    M. Spiliopoulou and L. C. Faulstich. WUM: A Web Utilization Miner. In Proceedings of EDBT Workshop WebDB98, Valencia, Spain, LNCS 1590, Springer Verlag, 1999.Google Scholar
  13. 13.
    J. Srivastava, R. Cooley, M. Deshpande, P-T. Tan. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, (1) 2, 2000.Google Scholar
  14. 14.
    S. Schechter, M. Krishnan, and M. D. Smith. Using path profiles to predict HTTP requests. In Proceedings of 7th International World Wide Web Conference, Brisbane, Australia, 1998.Google Scholar
  15. 15.
    G. Salton, M.J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.Google Scholar
  16. 16.
    U. Shardanand, P. Maes. Social information filtering: algorithms for automating word of mouth. In Proceedings of the ACM CHI Conference, 1995.Google Scholar
  17. 17.
    C. Shahabi, A. Zarkesh, J. Adibi, and V. Shah. Knowledge discovery from users Web-page navigation. In Proceedings of Workshop on Research Issues in Data Engineering, Birmingham, England, 1997.Google Scholar
  18. 18.
    T. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal. From user access patterns to dynamic hypertext linking. In Proceedings of the 5th International World Wide Web Conference, Paris, France, 1996.Google Scholar
  19. 19.
    K. Wu, P. S. Yu, and A. Ballman. Speedtracer: A web usage mining and analysis tool. IBM Systems Journal, 37(1), 1998.Google Scholar
  20. 20.
    O. R. Zaiane, M. Xin, and J. Han. Discovering web access patterns and trends by applying olap and data mining technology on web logs. In Advances in Digital Libraries, pages 19–29, Santa Barbara, CA, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Bamshad Mobasher
    • 1
  • Honghua Dai
    • 1
  • Tao Luo
    • 1
  • Yuqing Sun
    • 1
  • Jiang Zhu
    • 1
  1. 1.School of Computer Science Telecommunications and Information SystemsDePaul UniversityChicagoUSA

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