Robust Bayesian matrix factorization and recommender systems using same

摘要

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.

主权项

1. An apparatus comprising:
an electronic data processing device configured to perform a recommender method including:
performing Bayesian Matrix Factorization (BMF) on a matrix having user and item dimensions using Gaussian scale mixture priors comprising Student-t priors to train a probabilistic collaborative filtering model; andgenerating a recommendation for a user comprising a predicted item rating or identification of one or more recommended items using the probabilistic collaborative filtering model.