Exploiting user and item embedding in latent factor models for recommendations

Matrix factorization (MF) models and their extensions are widely used in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose mixture models which combine the technology of MF and the embedding. We show that some of these models significantly improve the performance over the state-of-the-art models on two real-world datasets, and explain how the mixture models improve the quality of recommendations.