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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/135217
Ver los metadatos del registro completo
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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 |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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application/pdf 54-76 |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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