Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System

dc.contributor.authorLara Cabrera, Raúl
dc.contributor.authorGonzález, Álvaro
dc.contributor.authorOrtega Ojeda, Fernando
dc.contributor.authorGonzález Prieto, José Ángel
dc.date.accessioned2023-06-22T11:04:53Z
dc.date.available2023-06-22T11:04:53Z
dc.date.issued2022-01-24
dc.description.abstractTraditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classification task instead of as a regression. The new model, Dirichlet Matrix Factorization (DirMF), provides not only a prediction but also its reliability, hence achieving a better balance between the quality and quantity of the predictions (i.e., reducing the prediction error by limiting the model’s coverage). The experimental results conducted show that the proposed model outperforms other models due to its ability to discard unreliable predictions. Compared to our previous model, which uses the same classification approach, DirMF shows a similar efficiency, outperforming the former on some of the datasets included in the experimental setup.en
dc.description.departmentDepto. de Álgebra, Geometría y Topología
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades (España)/Fondo Europeo de Desarrollo Regional
dc.description.sponsorshipComunidad de Madrid
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/74845
dc.identifier.citationLara Cabrera, R., González, Á., Ortega Ojeda, F. & González Prieto, J. Á. «Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System». Applied Sciences, vol. 12, n.o 3, enero de 2022, p. 1223. DOI.org (Crossref), https://doi.org/10.3390/app12031223.
dc.identifier.doi10.3390/app12031223
dc.identifier.issn2076-3417
dc.identifier.officialurlhttps://doi.org/10.3390/app12031223
dc.identifier.relatedurlhttps://www.mdpi.com/2076-3417/12/3/1223/htm
dc.identifier.urihttps://hdl.handle.net/20.500.14352/72083
dc.issue.number3
dc.journal.titleApplied Sciences
dc.language.isoeng
dc.page.initial1223
dc.publisherMPDI
dc.relation.projectIDPID2019-106493RB-I00 (DL-CEMG)
dc.relation.projectIDConvenio Plurianual with the Universidad Politécnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/es/
dc.subject.keywordRecommender systems
dc.subject.keywordCollaborative filtering
dc.subject.keywordMatrix factorization
dc.subject.keywordReliability
dc.subject.keywordClassification model
dc.subject.keywordDirichlet distribution
dc.subject.ucmAnálisis funcional y teoría de operadores
dc.titleDirichlet Matrix Factorization: A Reliable Classification-Based Recommender Systemen
dc.typejournal article
dc.volume.number12
dspace.entity.typePublication
relation.isAuthorOfPublicationc3011bfd-5025-4e49-8f0e-e16ea76da35c
relation.isAuthorOfPublication.latestForDiscoveryc3011bfd-5025-4e49-8f0e-e16ea76da35c
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