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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)

Abstract

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.

Keywords

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.

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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

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