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)


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.


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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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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