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Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models

Author

Listed:
  • Alberto Lumbreras

    (Technicolor)

  • Julien Velcin

    (Université de Lyon)

  • Marie Guégan

    (Technicolor)

  • Bertrand Jouve

    (Université de Toulouse
    Université de Toulouse)

Abstract

We present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through user actions or behaviors. Our task is to infer the groups of users as well as their latent behavioral functions. We also propose a non-parametric version based on a Dirichlet Process to automatically infer the number of clusters. We test the properties and performance of the model on a synthetic dataset that represents the participation of users in the threads of an online forum. Experiments show that dual-view models outperform single-view ones when one of the views lacks information.

Suggested Citation

  • Alberto Lumbreras & Julien Velcin & Marie Guégan & Bertrand Jouve, 2017. "Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models," Computational Statistics, Springer, vol. 32(1), pages 145-177, March.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:1:d:10.1007_s00180-016-0668-0
    DOI: 10.1007/s00180-016-0668-0
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    References listed on IDEAS

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    1. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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    Cited by:

    1. İsmail Güzel & Atabey Kaygun, 2022. "A new non-archimedean metric on persistent homology," Computational Statistics, Springer, vol. 37(4), pages 1963-1983, September.

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