Using negotiation for group recommendation : A user-study on the movies domain

Autores
Villavicencio, Christian; Schiaffino, Silvia; Diaz Pace, J. Andrés; Monteserin, Ariel
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
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 dier with reality, we decided to assess MAGReS using data from real users. The results obtained showed rstly 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. Finally, we could obtain some preliminary feedback regarding the explanations provided by the recommender system.
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/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/135217

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spelling Using negotiation for group recommendation : A user-study on the movies domainVillavicencio, ChristianSchiaffino, SilviaDiaz 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 dier with reality, we decided to assess MAGReS using data from real users. The results obtained showed rstly 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. Finally, we could obtain some preliminary feedback regarding the explanations provided by the recommender system.Sociedad Argentina de Informática e Investigación Operativa2018-04-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf54-76http://sedici.unlp.edu.ar/handle/10915/135217enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/45info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/reference/hdl/10915/65948info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:34:00Zoai:sedici.unlp.edu.ar:10915/135217Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:34:00.968SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Using negotiation for group recommendation : A user-study on the movies domain
title Using negotiation for group recommendation : A user-study on the movies domain
spellingShingle Using negotiation for group recommendation : A user-study on the movies domain
Villavicencio, Christian
Ciencias Informáticas
sistema de recomendación
Intelligent agents
Multiagent systems
Grupo de usuarios
title_short Using negotiation for group recommendation : A user-study on the movies domain
title_full Using negotiation for group recommendation : A user-study on the movies domain
title_fullStr Using negotiation for group recommendation : A user-study on the movies domain
title_full_unstemmed Using negotiation for group recommendation : A user-study on the movies domain
title_sort Using negotiation for group recommendation : A user-study on the movies domain
dc.creator.none.fl_str_mv Villavicencio, Christian
Schiaffino, Silvia
Diaz Pace, J. Andrés
Monteserin, Ariel
author Villavicencio, Christian
author_facet Villavicencio, Christian
Schiaffino, Silvia
Diaz Pace, J. Andrés
Monteserin, Ariel
author_role author
author2 Schiaffino, Silvia
Diaz 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 dier with reality, we decided to assess MAGReS using data from real users. The results obtained showed rstly 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. Finally, we could obtain some preliminary feedback regarding the explanations provided by the recommender system.
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 dier with reality, we decided to assess MAGReS using data from real users. The results obtained showed rstly 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. Finally, we could obtain some preliminary feedback regarding the explanations provided by the recommender system.
publishDate 2018
dc.date.none.fl_str_mv 2018-04-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/135217
url http://sedici.unlp.edu.ar/handle/10915/135217
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/45
info:eu-repo/semantics/altIdentifier/issn/1514-6774
info:eu-repo/semantics/reference/hdl/10915/65948
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
54-76
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instname:Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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