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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21269

Title: Exploring Parallel Implementations of the Bayesian Probabilistic Matrix Factorization
Authors: Chakroun, Imen
Haber, Tom
Vander Aa, Tom
Kovac, Thomas
Issue Date: 2016
Publisher: IEEE Computer Society
Citation: 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), p. 119-126
Series/Report: Euromicro International Conference
Series/Report no.: 24
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 because of the prohibitive cost. In this paper, we propose a comprehensive parallel implementation of the BPMF using Gibbs sampling on shared and distributed architectures. We also propose an insight of a GPU-based implementation of this algorithm.
Notes: Chakroun, I (reprint author), IMEC, ExaSci Life Lab, Leuven, Belgium.
URI: http://hdl.handle.net/1942/21269
DOI: 10.1109/PDP.2016.48
ISI #: 000381810900015
ISBN: 9781467387750
ISSN: 1066-6192
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2017
Appears in Collections: Research publications

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