Scalable Recommendation with Poisson Factorization

We develop hierarchical Poisson matrix factorization (HPF) for recommendation. HPF models sparse user behavior data, large user/item matrices where each user has provided feedback on only a small subset of items. HPF handles both explicit ratings, such as a number of stars, or implicit ratings, such as views, clicks, or purchases. We develop a variational algorithm for approximate posterior inference that scales up to massive data sets, and we demonstrate its performance on a wide variety of real-world recommendation problems–users rating movies, users listening to songs, users reading scientific papers, and users reading news articles. Our study reveals that hierarchical Poisson factorization definitively outperforms previous methods, including nonnegative matrix factorization, topic models, and probabilistic matrix factorization techniques.