Deep variational models for collaborative filtering-based recommender systems
| Autores: | , , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Universidad Politécnica de Madrid |
| Repositorio: | Archivo Digital UPM |
| OAI Identifier: | oai:oa.upm.es:91910 |
| Acesso em linha: | https://oa.upm.es/91910/ |
| Access Level: | acceso abierto |
| Palavra-chave: | Recommender systems Collaborative filtering Variational enrichment Deep learning |
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oai:oa.upm.es:91910 |
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España |
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Deep variational models for collaborative filtering-based recommender systemsBobadilla Sancho, Jesús|||0000-0003-0619-1322Ortega Requena, Fernando|||0000-0003-4765-1479Gutiérrez Rodríguez, Abraham|||0000-0001-6974-7514González Prieto, José Ángel|||0000-0003-2326-6752González Prieto, Ángel|||0000-0003-0122-384XRecommender systemsCollaborative filteringVariational enrichmentDeep learning20232023-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articlehttps://oa.upm.es/91910/reponame:Archivo Digital UPMinstname:Universidad Politécnica de MadridInglésenopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:oa.upm.es:919102026-06-21T12:45:07Z |
| dc.title.none.fl_str_mv |
Deep variational models for collaborative filtering-based recommender systems |
| title |
Deep variational models for collaborative filtering-based recommender systems |
| spellingShingle |
Deep variational models for collaborative filtering-based recommender systems Bobadilla Sancho, Jesús|||0000-0003-0619-1322 Recommender systems Collaborative filtering Variational enrichment Deep learning |
| title_short |
Deep variational models for collaborative filtering-based recommender systems |
| title_full |
Deep variational models for collaborative filtering-based recommender systems |
| title_fullStr |
Deep variational models for collaborative filtering-based recommender systems |
| title_full_unstemmed |
Deep variational models for collaborative filtering-based recommender systems |
| title_sort |
Deep variational models for collaborative filtering-based recommender systems |
| dc.creator.none.fl_str_mv |
Bobadilla Sancho, Jesús|||0000-0003-0619-1322 Ortega Requena, Fernando|||0000-0003-4765-1479 Gutiérrez Rodríguez, Abraham|||0000-0001-6974-7514 González Prieto, José Ángel|||0000-0003-2326-6752 González Prieto, Ángel|||0000-0003-0122-384X |
| author |
Bobadilla Sancho, Jesús|||0000-0003-0619-1322 |
| author_facet |
Bobadilla Sancho, Jesús|||0000-0003-0619-1322 Ortega Requena, Fernando|||0000-0003-4765-1479 Gutiérrez Rodríguez, Abraham|||0000-0001-6974-7514 González Prieto, José Ángel|||0000-0003-2326-6752 González Prieto, Ángel|||0000-0003-0122-384X |
| author_role |
author |
| author2 |
Ortega Requena, Fernando|||0000-0003-4765-1479 Gutiérrez Rodríguez, Abraham|||0000-0001-6974-7514 González Prieto, José Ángel|||0000-0003-2326-6752 González Prieto, Ángel|||0000-0003-0122-384X |
| author2_role |
author author author author |
| dc.subject.none.fl_str_mv |
Recommender systems Collaborative filtering Variational enrichment Deep learning |
| topic |
Recommender systems Collaborative filtering Variational enrichment Deep learning |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2023-01-01 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://oa.upm.es/91910/ |
| url |
https://oa.upm.es/91910/ |
| dc.language.none.fl_str_mv |
Inglés en |
| language_invalid_str_mv |
Inglés en |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
open access http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.source.none.fl_str_mv |
reponame:Archivo Digital UPM instname:Universidad Politécnica de Madrid |
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Universidad Politécnica de Madrid |
| reponame_str |
Archivo Digital UPM |
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Archivo Digital UPM |
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1869403285801140224 |
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15,812429 |