Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes

TL;DR

This paper introduces a novel framework that integrates side information into probabilistic matrix factorization using Gaussian processes, enabling more accurate predictions in domains like sports and collaborative filtering.

Contribution

It proposes coupling multiple PMF problems with Gaussian process priors, replacing scalar features with functions that depend on covariates, allowing smooth sharing of information.

Findings

Improved prediction accuracy for basketball game scores.

Effective incorporation of side information in PMF models.

Demonstrated benefits in collaborative filtering scenarios.

Abstract

Probabilistic matrix factorization (PMF) is a powerful method for modeling data associ- ated with pairwise relationships, Finding use in collaborative Filtering, computational bi- ology, and document analysis, among other areas. In many domains, there are additional covariates that can assist in prediction. For example, when modeling movie ratings, we might know when the rating occurred, where the user lives, or what actors appear in the movie. It is difficult, however, to incorporate this side information into the PMF model. We propose a framework for incorporating side information by coupling together multi- ple PMF problems via Gaussian process priors. We replace scalar latent features with func- tions that vary over the covariate space. The GP priors on these functions require them to vary smoothly and share information. We apply this new method to predict the scores of professional basketball games, where side information about the venue and date of the game are relevant for the outcome.