Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
This work addresses the challenge of integrating additional covariates into PMF models for domains like collaborative filtering and sports analytics, representing an incremental improvement.
The authors tackled the problem of incorporating side information into probabilistic matrix factorization by proposing a framework that uses Gaussian process priors to couple multiple PMF problems, applying it to predict professional basketball game scores with relevant covariates like venue and date.
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.