Incorporating side information into probabilistic matrix factorization using Gaussian Processes

Ryan Prescott Adams, George E. Dahl, Iain Murray

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Probabilistic matrix factorization (PMF) is a powerful method for modeling data associated with pairwise relationships, finding use in collaborative filtering, computational biology, 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 multiple PMF problems via Gaussian process priors. We replace scalar latent features with functions 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.
Original languageEnglish
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Number of pages9
Publication statusPublished - 2010


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