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

## TL;DR

This paper introduces a dual-view mixture model with a non-parametric Dirichlet Process extension to cluster users based on features and latent behaviors, improving performance over single-view models especially when data is incomplete.

## Contribution

The paper proposes a novel dual-view mixture model with a non-parametric extension for automatic cluster number inference, enhancing user clustering accuracy.

## Key findings

- Dual-view models outperform single-view models when data is incomplete.
- The non-parametric model effectively infers the number of user clusters.
- Experiments validate the model's robustness on synthetic online forum data.

## 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.