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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/33705
Ver los metadatos del registro completo
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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 Técnicas2025-09-03T09:52:57Zoai: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:34982025-09-03 09:52:58.138CONICET 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 |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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1842269191163346944 |
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13.13397 |