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.
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Zhou, Y.K., Mobasher, B. (2006). Web User Segmentation Based on a Mixture of Factor Analyzers. In: Bauknecht, K., Pröll, B., Werthner, H. (eds) E-Commerce and Web Technologies. EC-Web 2006. Lecture Notes in Computer Science, vol 4082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823865_2
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DOI: https://doi.org/10.1007/11823865_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37743-6
Online ISBN: 978-3-540-37745-0
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