Social Influence in Group Recommender Systems

Autores
Schiaffino, Silvia Noemi; Christensen, Ingrid Alina
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Purpose – The purpose of this paper is to propose an approach to generate recommendations for groups on the basis of social factors extracted from a social network. Group recommendation techniques traditionally assumed users were independent individuals, ignoring the effects of social interaction and relationships among users. In this work the authors analyse the social factors available in social networks in the light of sociological theories which endorse individuals’ susceptibility to influence within a group. Design/methodology/approach – The approach proposed is based on the creation of a group model in two stages: identifying the items that are representative of the majority's preferences, and analysing members’ similarity; and extracting potential influence from members’ interactions in a social network to predict a group's opinion on each item. Findings – The promising results obtained when evaluating the approach in the movie domain suggest that individual opinions tend to be accommodated to group satisfaction, as demonstrated by the incidence of the aforementioned factors in collective behaviour, as endorsed by sociological research. Moreover the findings suggest that these factors have dissimilar impacts on group satisfaction. Originality/value – The results obtained provide clues about how social influence exerted within groups could alter individuals’ opinions when a group has a common goal. There is limited research in this area exploring social influence in group recommendations; thus the originality of this perspective lies in the use of sociological theory to explain social influence in groups of users, and the flexibility of the approach to be applied in any domain. The findings could be helpful for group recommender systems developers both at research and commercial levels.
Fil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Christensen, Ingrid Alina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Materia
Social Influence
Group Recommendation
Social Recommendation
Online Social Networks
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:11336/6794

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network_name_str CONICET Digital (CONICET)
spelling Social Influence in Group Recommender SystemsSchiaffino, Silvia NoemiChristensen, Ingrid AlinaSocial InfluenceGroup RecommendationSocial RecommendationOnline Social Networkshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Purpose – The purpose of this paper is to propose an approach to generate recommendations for groups on the basis of social factors extracted from a social network. Group recommendation techniques traditionally assumed users were independent individuals, ignoring the effects of social interaction and relationships among users. In this work the authors analyse the social factors available in social networks in the light of sociological theories which endorse individuals’ susceptibility to influence within a group. Design/methodology/approach – The approach proposed is based on the creation of a group model in two stages: identifying the items that are representative of the majority's preferences, and analysing members’ similarity; and extracting potential influence from members’ interactions in a social network to predict a group's opinion on each item. Findings – The promising results obtained when evaluating the approach in the movie domain suggest that individual opinions tend to be accommodated to group satisfaction, as demonstrated by the incidence of the aforementioned factors in collective behaviour, as endorsed by sociological research. Moreover the findings suggest that these factors have dissimilar impacts on group satisfaction. Originality/value – The results obtained provide clues about how social influence exerted within groups could alter individuals’ opinions when a group has a common goal. There is limited research in this area exploring social influence in group recommendations; thus the originality of this perspective lies in the use of sociological theory to explain social influence in groups of users, and the flexibility of the approach to be applied in any domain. The findings could be helpful for group recommender systems developers both at research and commercial levels.Fil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Christensen, Ingrid Alina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaEmerald Group Publishing Limited2014-11info: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/11336/6794Schiaffino, Silvia Noemi; Christensen, Ingrid Alina; Social Influence in Group Recommender Systems; Emerald Group Publishing Limited; Online Information Review; 38; 4; 11-2014; 524-5421468-4527enginfo:eu-repo/semantics/altIdentifier/url/http://www.emeraldinsight.com/doi/abs/10.1108/OIR-08-2013-0187?journalCode=oirinfo:eu-repo/semantics/altIdentifier/doi/10.1108/OIR-08-2013-0187info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:47Zoai:ri.conicet.gov.ar:11336/6794instacron: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 09:47:47.413CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Social Influence in Group Recommender Systems
title Social Influence in Group Recommender Systems
spellingShingle Social Influence in Group Recommender Systems
Schiaffino, Silvia Noemi
Social Influence
Group Recommendation
Social Recommendation
Online Social Networks
title_short Social Influence in Group Recommender Systems
title_full Social Influence in Group Recommender Systems
title_fullStr Social Influence in Group Recommender Systems
title_full_unstemmed Social Influence in Group Recommender Systems
title_sort Social Influence in Group Recommender Systems
dc.creator.none.fl_str_mv Schiaffino, Silvia Noemi
Christensen, Ingrid Alina
author Schiaffino, Silvia Noemi
author_facet Schiaffino, Silvia Noemi
Christensen, Ingrid Alina
author_role author
author2 Christensen, Ingrid Alina
author2_role author
dc.subject.none.fl_str_mv Social Influence
Group Recommendation
Social Recommendation
Online Social Networks
topic Social Influence
Group Recommendation
Social Recommendation
Online Social Networks
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Purpose – The purpose of this paper is to propose an approach to generate recommendations for groups on the basis of social factors extracted from a social network. Group recommendation techniques traditionally assumed users were independent individuals, ignoring the effects of social interaction and relationships among users. In this work the authors analyse the social factors available in social networks in the light of sociological theories which endorse individuals’ susceptibility to influence within a group. Design/methodology/approach – The approach proposed is based on the creation of a group model in two stages: identifying the items that are representative of the majority's preferences, and analysing members’ similarity; and extracting potential influence from members’ interactions in a social network to predict a group's opinion on each item. Findings – The promising results obtained when evaluating the approach in the movie domain suggest that individual opinions tend to be accommodated to group satisfaction, as demonstrated by the incidence of the aforementioned factors in collective behaviour, as endorsed by sociological research. Moreover the findings suggest that these factors have dissimilar impacts on group satisfaction. Originality/value – The results obtained provide clues about how social influence exerted within groups could alter individuals’ opinions when a group has a common goal. There is limited research in this area exploring social influence in group recommendations; thus the originality of this perspective lies in the use of sociological theory to explain social influence in groups of users, and the flexibility of the approach to be applied in any domain. The findings could be helpful for group recommender systems developers both at research and commercial levels.
Fil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Christensen, Ingrid Alina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
description Purpose – The purpose of this paper is to propose an approach to generate recommendations for groups on the basis of social factors extracted from a social network. Group recommendation techniques traditionally assumed users were independent individuals, ignoring the effects of social interaction and relationships among users. In this work the authors analyse the social factors available in social networks in the light of sociological theories which endorse individuals’ susceptibility to influence within a group. Design/methodology/approach – The approach proposed is based on the creation of a group model in two stages: identifying the items that are representative of the majority's preferences, and analysing members’ similarity; and extracting potential influence from members’ interactions in a social network to predict a group's opinion on each item. Findings – The promising results obtained when evaluating the approach in the movie domain suggest that individual opinions tend to be accommodated to group satisfaction, as demonstrated by the incidence of the aforementioned factors in collective behaviour, as endorsed by sociological research. Moreover the findings suggest that these factors have dissimilar impacts on group satisfaction. Originality/value – The results obtained provide clues about how social influence exerted within groups could alter individuals’ opinions when a group has a common goal. There is limited research in this area exploring social influence in group recommendations; thus the originality of this perspective lies in the use of sociological theory to explain social influence in groups of users, and the flexibility of the approach to be applied in any domain. The findings could be helpful for group recommender systems developers both at research and commercial levels.
publishDate 2014
dc.date.none.fl_str_mv 2014-11
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/11336/6794
Schiaffino, Silvia Noemi; Christensen, Ingrid Alina; Social Influence in Group Recommender Systems; Emerald Group Publishing Limited; Online Information Review; 38; 4; 11-2014; 524-542
1468-4527
url http://hdl.handle.net/11336/6794
identifier_str_mv Schiaffino, Silvia Noemi; Christensen, Ingrid Alina; Social Influence in Group Recommender Systems; Emerald Group Publishing Limited; Online Information Review; 38; 4; 11-2014; 524-542
1468-4527
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.emeraldinsight.com/doi/abs/10.1108/OIR-08-2013-0187?journalCode=oir
info:eu-repo/semantics/altIdentifier/doi/10.1108/OIR-08-2013-0187
info:eu-repo/semantics/altIdentifier/doi/
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 Emerald Group Publishing Limited
publisher.none.fl_str_mv Emerald Group Publishing Limited
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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