Summary: Incorporating Side Information in Probabilistic Matrix Factorization
with Gaussian Processes
Ryan Prescott Adams
George E. Dahl
Department of Computer Science
University of Toronto
Toronto, Canada
Iain Murray
School of Informatics
University of Edinburgh
Edinburgh, Scotland
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

Source: Adams, Ryan Prescott - Department of Electrical and Computer Engineering, University of Toronto
Edinburgh, University of - Division of Informatics, Institute for Adaptive and Neural Computation
Roweis, Sam - Department of Computer Science, University of Toronto

Collections: Biology and Medicine; Computer Technologies and Information Sciences