Patent Number: 8,880,439

Title: Robust Bayesian matrix factorization and recommender systems using same

Abstract: In a recommender method, Bayesian Matrix Factorization (BMF) is performed on a matrix having user and item dimensions and matrix elements containing user ratings for items made by users in order to train a probabilistic collaborative filtering model. A recommendation is generated for a user using the probabilistic collaborative filtering model. The recommendation may comprise a predicted item rating, or an identification of one or more recommended items. The recommender method is suitably performed by an electronic data processing device. The BMF may employ non-Gaussian priors, such as Student-t priors. The BMF may additionally or alternatively employ a heteroscedastic noise model comprising priors that include (1) a row dependent variance component that depends upon the matrix row and (2) a column dependent variance component that depends upon the matrix column.

Inventors: Archambeau; Cedric (Grenoble, FR), Bouchard; Guillaume (Crolles, FR), Lakshminarayanan; Balaji (London, GB)

Assignee: Xerox Corporation

International Classification: G06F 15/18 (20060101)

Expiration Date: 2019-11-04 0:00:00