Robust fuzzy factorization machine with noise clustering-based membership function estimation

Factorization machine (FM) is a promising model-based algorithm for collaborative filtering (CF), but can bring inferior performances if datasets include users having low confidence. In this paper, a robust FM model is proposed by introducing the noise clustering-based noise rejection mechanism into...

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Autores principales:	Katsuhiro Honda, Keita Hoshii, Seiki Ubukata, Akira Notsu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Collaborative filtering Factorization machines Membership function Noise clustering Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/5fe92a059d4644cea2951cfcbe7b516d
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id	oai:doaj.org-article:5fe92a059d4644cea2951cfcbe7b516d
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spelling	oai:doaj.org-article:5fe92a059d4644cea2951cfcbe7b516d2021-11-14T04:35:41ZRobust fuzzy factorization machine with noise clustering-based membership function estimation2666-222110.1016/j.socl.2021.100024https://doaj.org/article/5fe92a059d4644cea2951cfcbe7b516d2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666222121000137https://doaj.org/toc/2666-2221Factorization machine (FM) is a promising model-based algorithm for collaborative filtering (CF), but can bring inferior performances if datasets include users having low confidence. In this paper, a robust FM model is proposed by introducing the noise clustering-based noise rejection mechanism into Fuzzy FM, which utilizes fuzzy memberships of users for considering the responsibility of each user in FM modeling. By automatically updating fuzzy memberships with user-wise criteria of prediction errors, the FM model is better fitted to reliable users and is expected to improve the generalization ability for predicting the preference degrees of unknown items. The characteristics of the proposed method are demonstrated through numerical experiments with MovieLens movie evaluation data such that the prediction ability for not only the training ratings but also the test ratings of reliable users can be improved by carefully tuning the noise sensitivity weight.Katsuhiro HondaKeita HoshiiSeiki UbukataAkira NotsuElsevierarticleCollaborative filteringFactorization machinesMembership functionNoise clusteringInformation technologyT58.5-58.64Electronic computers. Computer scienceQA75.5-76.95ENSoft Computing Letters, Vol 3, Iss , Pp 100024- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Collaborative filtering Factorization machines Membership function Noise clustering Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Collaborative filtering Factorization machines Membership function Noise clustering Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95 Katsuhiro Honda Keita Hoshii Seiki Ubukata Akira Notsu Robust fuzzy factorization machine with noise clustering-based membership function estimation
description	Factorization machine (FM) is a promising model-based algorithm for collaborative filtering (CF), but can bring inferior performances if datasets include users having low confidence. In this paper, a robust FM model is proposed by introducing the noise clustering-based noise rejection mechanism into Fuzzy FM, which utilizes fuzzy memberships of users for considering the responsibility of each user in FM modeling. By automatically updating fuzzy memberships with user-wise criteria of prediction errors, the FM model is better fitted to reliable users and is expected to improve the generalization ability for predicting the preference degrees of unknown items. The characteristics of the proposed method are demonstrated through numerical experiments with MovieLens movie evaluation data such that the prediction ability for not only the training ratings but also the test ratings of reliable users can be improved by carefully tuning the noise sensitivity weight.
format	article
author	Katsuhiro Honda Keita Hoshii Seiki Ubukata Akira Notsu
author_facet	Katsuhiro Honda Keita Hoshii Seiki Ubukata Akira Notsu
author_sort	Katsuhiro Honda
title	Robust fuzzy factorization machine with noise clustering-based membership function estimation
title_short	Robust fuzzy factorization machine with noise clustering-based membership function estimation
title_full	Robust fuzzy factorization machine with noise clustering-based membership function estimation
title_fullStr	Robust fuzzy factorization machine with noise clustering-based membership function estimation
title_full_unstemmed	Robust fuzzy factorization machine with noise clustering-based membership function estimation
title_sort	robust fuzzy factorization machine with noise clustering-based membership function estimation
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5fe92a059d4644cea2951cfcbe7b516d
work_keys_str_mv	AT katsuhirohonda robustfuzzyfactorizationmachinewithnoiseclusteringbasedmembershipfunctionestimation AT keitahoshii robustfuzzyfactorizationmachinewithnoiseclusteringbasedmembershipfunctionestimation AT seikiubukata robustfuzzyfactorizationmachinewithnoiseclusteringbasedmembershipfunctionestimation AT akiranotsu robustfuzzyfactorizationmachinewithnoiseclusteringbasedmembershipfunctionestimation
_version_	1718429899804377088

Robust fuzzy factorization machine with noise clustering-based membership function estimation

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