Gaussian-Gamma collaborative filtering: A hierarchical Bayesian model for recommender systems

Highlights

• The Gamma-Gaussian assumption on the ratings. It is a heavy tail distribution so the model is more robust.

• The Gamma-Gaussian assumption on the latent features. Hence we do not need to specify the regularization term manually.

• The Gibbs sampling of the parameters and the statistical explanation of the updating formulas.

Abstract

The traditional collaborative filtering (CF) suffers from two key challenges, namely, the normal assumption that it is not robust, and it is difficult to set in advance the penalty terms of the latent features. We therefore propose a hierarchical Bayesian model-based CF and the related inference algorithm. Specifically, we impose a Gaussian-Gamma prior on the ratings, and the latent features. We show the model is more robust, and the penalty terms can be adapted automatically in the inference. We use Gibbs sampler for the inference and provide a statistical explanation. We verify the performance using both synthetic and real datasets.