Abstract

Discrete mixed membership modeling and continuous latent factor modeling (also known as matrix factorization) are two popular, complementary approaches to dyadic data analysis. In this work, we develop a fully Bayesian framework for integrating the two approaches into unified Mixed Membership Matrix Factorization (M3 F) models. We introduce two M3 F models, derive Gibbs sampling inference procedures, and validate our methods on the EachMovie, MovieLens, and Netflix Prize collaborative filtering datasets. We find that, even when fitting fewer parameters, the M3 F models outperform state-ofthe-art latent factor approaches on all benchmarks, yielding the greatest gains in accuracy on sparsely-rated, high-variance items.