A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data

TL;DR: This paper presents a hierarchical Bayesian model that can infer the latent feature vectors of items directly from the implicit feedback when they cannot be obtained from the rating data, and infer the full posterior distributions of these parameters using a Gibbs sampling method.

Abstract: Matrix factorization (MF) is one of the most efficient methods for performing collaborative filtering. An MF-based method represents users and items by latent feature vectors that are obtained by decomposing the rating matrix of users to items. However, MF-based methods suffer from the cold-start problem: if no rating data are available for an item, the model cannot find a latent feature vector for that item, and thus cannot make a recommendation for it. In this paper, we present a hierarchical Bayesian model that can infer the latent feature vectors of items directly from the implicit feedback (e.g., clicks, views, purchases) when they cannot be obtained from the rating data. We infer the full posterior distributions of these parameters using a Gibbs sampling method. We show that the proposed method is strong with overfitting even if the model is very complex or the data are very sparse. Our experiments on real-world datasets demonstrate that our proposed method significantly outperforms competing methods on rating prediction tasks, especially for very sparse datasets.