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2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) (2016)
Heraklion, Crete, Greece
Feb. 17, 2016 to Feb. 19, 2016
ISSN: 2377-5750
ISBN: 978-1-4673-8775-0
pp: 119-126
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.
INDEX TERMS
Instruction sets, Bayes methods, Recommender systems, Motion pictures, Electronics packaging, Multicore processing, Probabilistic logic
CITATION

I. Chakroun, T. Haber, T. V. Aa and T. Kovac, "Exploring Parallel Implementations of the Bayesian Probabilistic Matrix Factorization," 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), Heraklion, Crete, Greece, 2016, pp. 119-126.
doi:10.1109/PDP.2016.48
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