Exploring Parallel Implementations of the Bayesian Probabilistic Matrix Factorization - Chakroun, Imen; Haber, Tom; Aa, Tom Vander; Kovac, Thomas (2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), 2016)
Using the matrix factorization technique in machine learning is very common mainly in areas like recommender systems. Despite its high prediction accuracy and its ability to avoid over-fitting of the data, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used because of the prohibitive cost. In this paper, we propose a comprehensive parallel implementation of the BPMF using Gibbs sampling on shared and distributed architectures. We also propose an insight of a GPU-based implementation of this algorithm.