Web User Segmentation Based on a Mixture of Factor Analyzers

  • Yanzan Kevin Zhou
  • Bamshad Mobasher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4082)


This paper proposes an approach for Web user segmentation and online behavior analysis based on a mixture of factor analyzers (MFA). In our proposed framework, we model users’ shared interests as a set of common latent factors extracted through factor analysis, and we discover user segments based on the posterior component distribution of a finite mixture model. This allows us to measure the relationships between users’ unobserved conceptual interests and their observed navigational behavior in a principled probabilistic manner. Our experimental results show that the MFA-based approach results in finer-grained representation of user behavior and can successfully discover heterogeneous user segments and characterize these segments with respect to their common preferences.


Mixture Model Association Rule Latent Variable Model User Session User Segment 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems 1(1) (1999)Google Scholar
  2. 2.
    Frey, B.J., Colmenarez, A., Huang, T.S.: Mixtures of local linear subspaces for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos (June 1998)Google Scholar
  3. 3.
    Ghahramani, Z., Hinton, G.: The EM algorithm for mixture of factor analyzers. Technical report CRG-TR-96-1, University of Toronto (1996)Google Scholar
  4. 4.
    Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery 6, 83–105 (2002)CrossRefMathSciNetGoogle Scholar
  5. 5.
    McLachlan, G., Peel, D.: Mixtures of factor analyzers. In: Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, USA (2000)Google Scholar
  6. 6.
    Mobasher, B.: Web usage mining and personalization. In: Singh, M.P. (ed.) Practical Handbook of Internet Computing. CRC Press, Boca Raton (2005)Google Scholar
  7. 7.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalization based on association rule discovery from web usage data. In: Proceedings of the 3rd ACM Workshop on Web Information and Data Management (WIDM 2001), Atlanta, Georgia (November 2001)Google Scholar
  8. 8.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery 6, 61–82 (2002)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Saul, L.K., Rahim, M.G.: Modeling acoustic correlations by factor analysis. In: Jordan, M.I., Kearn, M.S., Solla, S.A. (eds.) Advances in Neural Information Processing Systems 10, pp. 749–756. MIT Press, Cambridge (1998)Google Scholar
  10. 10.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1(2), 12–23 (2000)CrossRefGoogle Scholar
  11. 11.
    Wedel, M., Kamakura, W.: Market Segmentation: Conceptual and Methodological Foundations. Springer, Heidelberg (1999)Google Scholar
  12. 12.
    Zhou, Y., Jin, X., Mobasher, B.: A recommendation model based on latent principal factors in web navigation data. In: Proceedings of the 3rd International Workshop on Web Dynamics at WWW 2004, New York (May 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yanzan Kevin Zhou
    • 1
  • Bamshad Mobasher
    • 2
  1. 1.eBay Inc.San Jose
  2. 2.DePaul UniversityChicago

Personalised recommendations