A Hybrid Approach for Group Profiling in Recommender Systems

Autores
Christensen, Ingrid Alina; Schiaffino, Silvia Noemi
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recommendation is a significant paradigm for information exploring, which focuses on the recovery of items of potential interest to users. Some activities tend to be social rather than individual, which puts forward the need to offer recommendations to groups of users. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this paper, we present a hybrid approach based on group profiling for homogeneous and non-homogenous groups containing a few distant individual profiles among their members. This approach combines three familiar individual recommendation approaches: collaborative filtering, content-based filtering and demographic information. This hybrid approach allows the detection of those implicit similarities in the user rating profile, so as to include members with divergent profiles. We also describe the promising results obtained when evaluating the approach proposed in the movie and music domain.
Fil: Christensen, Ingrid Alina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Materia
GROUP PROFILING
GROUP RECOMMENDER SYSTEMS
AGGREGATE RATINGS
HYBRID RECOMMENDER SYSTEMS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/33705
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A Hybrid Approach for Group Profiling in Recommender SystemsChristensen, Ingrid AlinaSchiaffino, Silvia NoemiGROUP PROFILINGGROUP RECOMMENDER SYSTEMSAGGREGATE RATINGSHYBRID RECOMMENDER SYSTEMShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Recommendation is a significant paradigm for information exploring, which focuses on the recovery of items of potential interest to users. Some activities tend to be social rather than individual, which puts forward the need to offer recommendations to groups of users. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this paper, we present a hybrid approach based on group profiling for homogeneous and non-homogenous groups containing a few distant individual profiles among their members. This approach combines three familiar individual recommendation approaches: collaborative filtering, content-based filtering and demographic information. This hybrid approach allows the detection of those implicit similarities in the user rating profile, so as to include members with divergent profiles. We also describe the promising results obtained when evaluating the approach proposed in the movie and music domain.Fil: Christensen, Ingrid Alina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaGraz University of Technology2014-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/33705Christensen, Ingrid Alina; Schiaffino, Silvia Noemi; A Hybrid Approach for Group Profiling in Recommender Systems; Graz University of Technology; Journal of Universal Computer Science; 20; 4; 4-2014; 507-5330948-695XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3217/jucs-020-04-0507info:eu-repo/semantics/altIdentifier/url/http://www.jucs.org/jucs_20_4/a_hybrid_approach_forinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:51:23Zoai:ri.conicet.gov.ar:11336/33705instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:51:23.844CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Hybrid Approach for Group Profiling in Recommender Systems
title A Hybrid Approach for Group Profiling in Recommender Systems
spellingShingle A Hybrid Approach for Group Profiling in Recommender Systems
Christensen, Ingrid Alina
GROUP PROFILING
GROUP RECOMMENDER SYSTEMS
AGGREGATE RATINGS
HYBRID RECOMMENDER SYSTEMS
title_short A Hybrid Approach for Group Profiling in Recommender Systems
title_full A Hybrid Approach for Group Profiling in Recommender Systems
title_fullStr A Hybrid Approach for Group Profiling in Recommender Systems
title_full_unstemmed A Hybrid Approach for Group Profiling in Recommender Systems
title_sort A Hybrid Approach for Group Profiling in Recommender Systems
dc.creator.none.fl_str_mv Christensen, Ingrid Alina
Schiaffino, Silvia Noemi
author Christensen, Ingrid Alina
author_facet Christensen, Ingrid Alina
Schiaffino, Silvia Noemi
author_role author
author2 Schiaffino, Silvia Noemi
author2_role author
dc.subject.none.fl_str_mv GROUP PROFILING
GROUP RECOMMENDER SYSTEMS
AGGREGATE RATINGS
HYBRID RECOMMENDER SYSTEMS
topic GROUP PROFILING
GROUP RECOMMENDER SYSTEMS
AGGREGATE RATINGS
HYBRID RECOMMENDER SYSTEMS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Recommendation is a significant paradigm for information exploring, which focuses on the recovery of items of potential interest to users. Some activities tend to be social rather than individual, which puts forward the need to offer recommendations to groups of users. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this paper, we present a hybrid approach based on group profiling for homogeneous and non-homogenous groups containing a few distant individual profiles among their members. This approach combines three familiar individual recommendation approaches: collaborative filtering, content-based filtering and demographic information. This hybrid approach allows the detection of those implicit similarities in the user rating profile, so as to include members with divergent profiles. We also describe the promising results obtained when evaluating the approach proposed in the movie and music domain.
Fil: Christensen, Ingrid Alina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
description Recommendation is a significant paradigm for information exploring, which focuses on the recovery of items of potential interest to users. Some activities tend to be social rather than individual, which puts forward the need to offer recommendations to groups of users. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this paper, we present a hybrid approach based on group profiling for homogeneous and non-homogenous groups containing a few distant individual profiles among their members. This approach combines three familiar individual recommendation approaches: collaborative filtering, content-based filtering and demographic information. This hybrid approach allows the detection of those implicit similarities in the user rating profile, so as to include members with divergent profiles. We also describe the promising results obtained when evaluating the approach proposed in the movie and music domain.
publishDate 2014
dc.date.none.fl_str_mv 2014-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/33705
Christensen, Ingrid Alina; Schiaffino, Silvia Noemi; A Hybrid Approach for Group Profiling in Recommender Systems; Graz University of Technology; Journal of Universal Computer Science; 20; 4; 4-2014; 507-533
0948-695X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/33705
identifier_str_mv Christensen, Ingrid Alina; Schiaffino, Silvia Noemi; A Hybrid Approach for Group Profiling in Recommender Systems; Graz University of Technology; Journal of Universal Computer Science; 20; 4; 4-2014; 507-533
0948-695X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3217/jucs-020-04-0507
info:eu-repo/semantics/altIdentifier/url/http://www.jucs.org/jucs_20_4/a_hybrid_approach_for
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Graz University of Technology
publisher.none.fl_str_mv Graz University of Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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