Dynamic Collaborative Filtering With Compound Poisson Factorization

Abstract

Model-based collaborative filtering (CF) analyzes user–item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most CF approaches assume that these latent factors are static; however, in most CF data, user preferences and item perceptions drift over time. Here, we propose a new conjugate and numerically stable dynamic matrix factorization (DCPF) based on hierarchical Poisson factorization that models the smoothly drifting latent factors using gamma-Markov chains. We propose a conjugate gamma chain construction that is numerically stable within our compound-Poisson framework. We then derive a scalable stochastic variational inference approach to estimate the parameters of our model. We apply our model to time-stamped ratings data sets from Netflix, Yelp, and Last.fm. We empirically demonstrate that DCPF achieves a higher predictive accuracy than state-of-the-art static and dynamic factorization algorithms.