2016 IEEE International Conference on Cluster Computing (CLUSTER) (2016)
Sept. 12, 2016 to Sept. 16, 2016
Matrix factorization is a common machine learning technique for recommender systems. Despite its high prediction accuracy, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used on large scale data because of its high computational cost. In this paper we propose a distributed high-performance parallel implementation of BPMF on shared memory and distributed architectures. We show by using efficient load balancing using work stealing on a single node, and by using asynchronous communication in the distributed version we beat state of the art implementations.
Motion pictures, Bayes methods, Libraries, Probabilistic logic, Recommender systems, Collaboration, Benchmark testing
T. V. Aa, I. Chakroun and T. Haber, "Distributed Bayesian Probabilistic Matrix Factorization," 2016 IEEE International Conference on Cluster Computing (CLUSTER), Taipei, Taiwan, 2016, pp. 346-349.