Distributed Bayesian Probabilistic Matrix Factorization

Using the matrix factorization technique in machine learning is very common mainly in areas like rec-ommender 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...

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Bibliographic Details
Published in:Procedia computer science Vol. 108; pp. 1030 - 1039
Main Authors: Vander Aa, Tom, Chakroun, Imen, Haber, Tom
Format: Journal Article
Language:English
Published: Elsevier B.V 2017
Subjects:
Collaborative filtering
Machine learning
multi-core
PGAS
Probabilistic matrix factorization algorithm
multi-core
Collaborative filtering
PGAS
Probabilistic matrix factorization algorithm
Machine learning
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary:Using the matrix factorization technique in machine learning is very common mainly in areas like rec-ommender 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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2017.05.009