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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
Citation: 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.
URI: http://hdl.handle.net/1942/23987
DOI: 10.1109/CLUSTER.2016.13
ISI #: 000391414100056
ISBN: 9781509036530
ISSN: 1552-5244
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2018
Appears in Collections: Research publications

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