MapReduce implementation of Variational Bayesian Probabilistic Matrix Factorization algorithm

Abstract

We introduce in this paper a scalable implementation of Variational Bayesian Matrix Factorization method for collaborative filtering using the MapReduce framework. Variational Bayesian methods have the advantage of providing good approximate analytical solutions for the posterior distribution. Due to the independence assumption about the parameters in the posterior distribution, variational methods are also likely to be able to parallelize efficiently. Though Variational Bayesian Matrix Factorization method has shown to produce more accurate results in collaborative filtering, its scaling properties have not studied so far. We ran our MapReduce implementation on the CiteULike data set and show that our parallelization scheme achieves approximately linear scaling. We also compare its performance with the MapReduce implementation of a popular matrix factorization algorithm, ALSWR, from the open source machine learning library Mahout.