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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 | Source: | 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. | Keywords: | collaborative filtering; machine learning; PGAS; probabilistic matrix factorization algorithm; multicore | Document URI: | http://hdl.handle.net/1942/21269 | ISBN: | 9781467387750 | DOI: | 10.1109/PDP.2016.48 | ISI #: | 000381810900015 | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2017 |
Appears in Collections: | Research publications |
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