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arXiv · 1705.10633

Distributed Matrix Factorization using Asynchrounous Communication

Tom Vander Aa·Imen Chakroun·Tom Haber
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Abstract

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 on large scale data because of the prohibitive cost. In this paper, we propose a distributed high-performance parallel implementation of the BPMF using Gibbs sampling on shared 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.

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Tom Vander Aa, Imen Chakroun, Tom Haber. 2017-05-29. Distributed Matrix Factorization using Asynchrounous Communication. https://doi.org/10.1016/j.procs.2017.05.009

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