Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23987
Title: Distributed Bayesian Probabilistic Matrix Factorization
Authors: Vander Aa, Tom
Chakroun, Imen
HABER, Tom 
Issue Date: 2016
Publisher: IEEE
Source: 2016 IEEE International Conference on Cluster Computing (CLUSTER), IEEE,p. 346-349
Series/Report: IEEE International Conference on Cluster Computing
Abstract: 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.
Keywords: probabilistic matrix factorization; collaborative filtering; machine learning; distributed systems; multi-core
Document URI: http://hdl.handle.net/1942/23987
ISBN: 9781509036530
DOI: 10.1109/CLUSTER.2016.13
ISI #: 000391414100056
Rights: © 2016 IEEE
Category: C1
Type: Proceedings Paper
Validations: ecoom 2018
Appears in Collections:Research publications

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