Exploring Parallel Implementations of the Bayesian Probabilistic Matrix Factorization

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 beca...

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Veröffentlicht in:2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) S. 119 - 126
Hauptverfasser: Chakroun, Imen, Haber, Tom, Aa, Tom Vander, Kovac, Thomas
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.02.2016
Schlagworte:
Algorithms
Bayes methods
Bayesian analysis
Collaborative filtering
Conferences
Electronics packaging
Factorization
Instruction sets
Machine learning
Motion pictures
multicore
Multicore processing
PGAS
Probabilistic logic
Probabilistic matrix factorization algorithm
Probabilistic methods
Probability theory
Recommender systems
Sampling
ISSN:2377-5750
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Zusammenfassung: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.
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SourceType-Conference Papers & Proceedings-2
ISSN:2377-5750
DOI:10.1109/PDP.2016.48