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http://hdl.handle.net/1942/26395
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DC Field | Value | Language |
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dc.contributor.author | Aa, Tom Vander | - |
dc.contributor.author | Chakroun, Imen | - |
dc.contributor.author | HABER, Tom | - |
dc.date.accessioned | 2018-07-20T14:53:55Z | - |
dc.date.available | 2018-07-20T14:53:55Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Koumoutsakos, Petros; Lees, Michael; Krzhizhanovskaya, Valeria; Dongarra, Jack; Sloot, Peter M. A. (Ed.). International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, Elsevier Science BV,p. 1030-1039 | - |
dc.identifier.issn | 1877-0509 | - |
dc.identifier.uri | http://hdl.handle.net/1942/26395 | - |
dc.description.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 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. (C) 2017 The Authors. Published by Elsevier B.V. | - |
dc.description.sponsorship | This work is partly funded by the European project ExCAPE with reference 671555. | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Science BV | - |
dc.relation.ispartofseries | Procedia Computer Science | - |
dc.rights | © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science | - |
dc.subject.other | Probabilistic matrix factorization algorithm; Collaborative filtering; Machine learning; PGAS; multi-core | - |
dc.subject.other | probabilistic matrix factorization algorithm; Collaborative filtering; machine learning; PGAS; multi-core | - |
dc.title | Distributed Bayesian Probabilistic Matrix Factorization | - |
dc.type | Proceedings Paper | - |
local.bibliographicCitation.authors | Koumoutsakos, Petros | - |
local.bibliographicCitation.authors | Lees, Michael | - |
local.bibliographicCitation.authors | Krzhizhanovskaya, Valeria | - |
local.bibliographicCitation.authors | Dongarra, Jack | - |
local.bibliographicCitation.authors | Sloot, Peter M. A. | - |
local.bibliographicCitation.conferencedate | 12-14/06/2017 | - |
local.bibliographicCitation.conferencename | International Conference on Computational Science (ICCS 2017) | - |
local.bibliographicCitation.conferenceplace | Zurich, Switzerland | - |
dc.identifier.epage | 1039 | - |
dc.identifier.spage | 1030 | - |
dc.identifier.volume | 108 | - |
local.format.pages | 10 | - |
local.bibliographicCitation.jcat | C1 | - |
dc.description.notes | [Aa, Tom Vander; Chakroun, Imen] IMEC, Exascience Lab, Kapeldreef 75, B-3001 Leuven, Belgium. [Haber, Tom] Expertise Ctr Digital Media, Wetenschapspk 2, B-3590 Diepenbeek, Belgium. | - |
local.publisher.place | Amsterdam, The Netherlands | - |
local.type.refereed | Refereed | - |
local.type.specified | Proceedings Paper | - |
local.relation.ispartofseriesnr | 108 | - |
local.class | dsPublValOverrule/author_version_not_expected | - |
dc.identifier.doi | 10.1016/j.procs.2017.05.009 | - |
dc.identifier.isi | 000404959000104 | - |
local.bibliographicCitation.btitle | International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland | - |
item.contributor | Aa, Tom Vander | - |
item.contributor | Chakroun, Imen | - |
item.contributor | HABER, Tom | - |
item.validation | ecoom 2018 | - |
item.fullcitation | Aa, Tom Vander; Chakroun, Imen & HABER, Tom (2017) Distributed Bayesian Probabilistic Matrix Factorization. In: Koumoutsakos, Petros; Lees, Michael; Krzhizhanovskaya, Valeria; Dongarra, Jack; Sloot, Peter M. A. (Ed.). International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, Elsevier Science BV,p. 1030-1039. | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Haber.pdf | Published version | 535.69 kB | Adobe PDF | View/Open |
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