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We present a dual-view mixture model to cluster users based on their featuresand latent behavioral functions. Every component of the mixture modelrepresents a probability density over a feature view for observed userattributes and a behavior view for latent behavioral functions that areindirectly observed through user actions or behaviors. Our task is to infer thegroups of users as well as their latent behavioral functions. We also propose anon-parametric version based on a Dirichlet Process to automatically infer thenumber of clusters. We test the properties and performance of the model on asynthetic dataset that represents the participation of users in the threads ofan online forum. Experiments show that dual-view models outperform single-viewones when one of the views lacks information.

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