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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Veröffentlicht in:Procedia computer science Jg. 108; S. 1030 - 1039
Hauptverfasser: Vander Aa, Tom, Chakroun, Imen, Haber, Tom
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 2017
Schlagworte:
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-Zugang:Volltext
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Beschreibung
Zusammenfassung: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