Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models
This work addresses clustering challenges in domains like online forums where user data has multiple aspects, but it is incremental as it builds on existing mixture model frameworks.
The paper tackles the problem of clustering users based on both observed features and latent behavioral functions, using a dual-view mixture model with a non-parametric extension to infer cluster numbers automatically. Experiments on a synthetic online forum dataset show that dual-view models outperform single-view ones when one view lacks information, though no concrete performance numbers are provided.
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.