Distributed Bayesian Probabilistic Matrix Factorization
Using the matrix factorization technique in machine learning is very common mainly in areas like rec-ommender 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 on...
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| Veröffentlicht in: | Procedia computer science Jg. 108; S. 1030 - 1039 |
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2017
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| Abstract | Using the matrix factorization technique in machine learning is very common mainly in areas like rec-ommender 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 on large scale data because of the prohibitive cost. In this paper, we propose a distributed high-performance parallel implementation of the BPMF using Gibbs sampling on shared 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. |
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| AbstractList | Using the matrix factorization technique in machine learning is very common mainly in areas like rec-ommender 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 on large scale data because of the prohibitive cost. In this paper, we propose a distributed high-performance parallel implementation of the BPMF using Gibbs sampling on shared 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. |
| Author | Chakroun, Imen Vander Aa, Tom Haber, Tom |
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| Cites_doi | 10.1016/j.dss.2013.04.002 10.1137/1.9781611971408 10.1137/S0036144502409019 10.1145/2020408.2020426 10.1016/j.ijforecast.2006.03.001 10.1093/nar/gkt1031 10.1109/MC.2009.263 10.1145/2827872 10.1145/2568088.2576761 10.1145/2783258.2783373 10.1007/978-3-540-68880-8_32 10.1109/CLUSTER.2016.13 10.1145/1390156.1390267 |
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| Keywords | multi-core Collaborative filtering PGAS Probabilistic matrix factorization algorithm Machine learning |
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Interact. Intell. Syst. doi: 10.1145/2827872 – ident: 10.1016/j.procs.2017.05.009_bib0007 doi: 10.1145/2568088.2576761 – ident: 10.1016/j.procs.2017.05.009_bib0001 doi: 10.1145/2783258.2783373 – ident: 10.1016/j.procs.2017.05.009_bib0022 doi: 10.1007/978-3-540-68880-8_32 – year: 2015 ident: 10.1016/j.procs.2017.05.009_bib0017 – ident: 10.1016/j.procs.2017.05.009_bib0019 doi: 10.1109/CLUSTER.2016.13 – ident: 10.1016/j.procs.2017.05.009_bib0005 – start-page: 1269 year: 2012 ident: 10.1016/j.procs.2017.05.009_bib0021 article-title: Bayesian group factor analysis publication-title: In AISTATS – ident: 10.1016/j.procs.2017.05.009_bib0006 – ident: 10.1016/j.procs.2017.05.009_bib0016 doi: 10.1145/1390156.1390267 |
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| Title | Distributed Bayesian Probabilistic Matrix Factorization |
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