Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas

Autores
Villavicencio, Christian; Schiaffino, Silvia; Díaz Pace, J. Andrés; Monteserin, Ariel
Año de publicación
2017
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Providing recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
sistema de recomendación
Intelligent agents
Multiagent systems
grupo de usuarios
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/65948
Acceder
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network_name_str SEDICI (UNLP)
spelling Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películasVillavicencio, ChristianSchiaffino, SilviaDíaz Pace, J. AndrésMonteserin, ArielCiencias Informáticassistema de recomendaciónIntelligent agentsMultiagent systemsgrupo de usuariosProviding recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members.Sociedad Argentina de Informática e Investigación Operativa2017-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf90-99http://sedici.unlp.edu.ar/handle/10915/65948spainfo:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/ASAI/asai-12.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7585info:eu-repo/semantics/reference/hdl/10915/135217info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/4.0/Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-17T09:52:35Zoai:sedici.unlp.edu.ar:10915/65948Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-17 09:52:36.066SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas
title Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas
spellingShingle Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas
Villavicencio, Christian
Ciencias Informáticas
sistema de recomendación
Intelligent agents
Multiagent systems
grupo de usuarios
title_short Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas
title_full Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas
title_fullStr Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas
title_full_unstemmed Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas
title_sort Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas
dc.creator.none.fl_str_mv Villavicencio, Christian
Schiaffino, Silvia
Díaz Pace, J. Andrés
Monteserin, Ariel
author Villavicencio, Christian
author_facet Villavicencio, Christian
Schiaffino, Silvia
Díaz Pace, J. Andrés
Monteserin, Ariel
author_role author
author2 Schiaffino, Silvia
Díaz Pace, J. Andrés
Monteserin, Ariel
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
sistema de recomendación
Intelligent agents
Multiagent systems
grupo de usuarios
topic Ciencias Informáticas
sistema de recomendación
Intelligent agents
Multiagent systems
grupo de usuarios
dc.description.none.fl_txt_mv Providing recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members.
Sociedad Argentina de Informática e Investigación Operativa
description Providing recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/65948
url http://sedici.unlp.edu.ar/handle/10915/65948
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info:eu-repo/semantics/altIdentifier/issn/2451-7585
info:eu-repo/semantics/reference/hdl/10915/135217
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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