Incorporating side information in probabilistic matrix factorization with Gaussian processes

Ryan Prescott Adams, George E. Dahl, Iain Murray

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

38 Scopus citations

Abstract

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, wemight know when the rating occurred, where the user lives, or what actors appear in the movie. It is dificult, 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 (US)
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
Pages1-9
Number of pages9
StatePublished - Dec 1 2010
Externally publishedYes
Event26th Conference on Uncertainty in Artificial Intelligence, UAI 2010 - Catalina Island, CA, United States
Duration: Jul 8 2010Jul 11 2010

Publication series

NameProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Other

Other26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
CountryUnited States
CityCatalina Island, CA
Period7/8/107/11/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Applied Mathematics

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  • Cite this

    Adams, R. P., Dahl, G. E., & Murray, I. (2010). Incorporating side information in probabilistic matrix factorization with Gaussian processes. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010 (pp. 1-9). (Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010).