Generalized Probabilistic Matrix Factorizations for Collaborative Filtering

Abstract

Probabilistic matrix factorization (PMF) have shown great promise in collaborative filtering. In this paper, we consider several variants and generalizations of PMF framework inspired by three broad questions: Are the prior distributions used in existing PMF models suitable, or can one get better predictive performance with different priors? Are there suitable extensions to leverage side information for prediction? Are there benefits to taking into account row and column biases, e.g., a critical user gives low ratings, and a popular movie gets high ratings in movie recommendation systems? We develop new families of PMF models to address these questions along with efficient approximate inference algorithms for learning and prediction. Through extensive experiments on movie recommendation datasets, we illustrate that simpler models directly capturing correlations among latent factors can outperform existing PMF models, side information can benefit prediction accuracy, and accounting for row/column biases leads to improvements in predictive performance.