A study of neighbour selection strategies for POI recommendation in LBSNs
- Autores
- Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis
- Año de publicación
- 2018
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.
Fil: Rios, Carlos. 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
Fil: Godoy, Daniela Lis. 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
-
LOCATION-BASED SOCIAL NETWORKS
RECOMMENDER SYSTEMS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR)
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/91004
Ver los metadatos del registro completo
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A study of neighbour selection strategies for POI recommendation in LBSNsRios, CarlosSchiaffino, Silvia NoemiGodoy, Daniela LisLOCATION-BASED SOCIAL NETWORKSRECOMMENDER SYSTEMShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.Fil: Rios, Carlos. 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; ArgentinaFil: Godoy, Daniela Lis. 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; ArgentinaSage Publications Ltd2018-03-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/91004Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis; A study of neighbour selection strategies for POI recommendation in LBSNs; Sage Publications Ltd; Journal Of Information Science; 44; 6; 5-3-2018; 802-8170165-5515CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://journals.sagepub.com/doi/10.1177/0165551518761000info:eu-repo/semantics/altIdentifier/doi/10.1177%2F0165551518761000info:eu-repo/semantics/openAccessAtribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR)https://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:43:46Zoai:ri.conicet.gov.ar:11336/91004instacron: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 09:43:46.541CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
A study of neighbour selection strategies for POI recommendation in LBSNs |
title |
A study of neighbour selection strategies for POI recommendation in LBSNs |
spellingShingle |
A study of neighbour selection strategies for POI recommendation in LBSNs Rios, Carlos LOCATION-BASED SOCIAL NETWORKS RECOMMENDER SYSTEMS |
title_short |
A study of neighbour selection strategies for POI recommendation in LBSNs |
title_full |
A study of neighbour selection strategies for POI recommendation in LBSNs |
title_fullStr |
A study of neighbour selection strategies for POI recommendation in LBSNs |
title_full_unstemmed |
A study of neighbour selection strategies for POI recommendation in LBSNs |
title_sort |
A study of neighbour selection strategies for POI recommendation in LBSNs |
dc.creator.none.fl_str_mv |
Rios, Carlos Schiaffino, Silvia Noemi Godoy, Daniela Lis |
author |
Rios, Carlos |
author_facet |
Rios, Carlos Schiaffino, Silvia Noemi Godoy, Daniela Lis |
author_role |
author |
author2 |
Schiaffino, Silvia Noemi Godoy, Daniela Lis |
author2_role |
author author |
dc.subject.none.fl_str_mv |
LOCATION-BASED SOCIAL NETWORKS RECOMMENDER SYSTEMS |
topic |
LOCATION-BASED SOCIAL NETWORKS 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 |
Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs. Fil: Rios, Carlos. 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 Fil: Godoy, Daniela Lis. 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 |
Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-03-05 |
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/11336/91004 Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis; A study of neighbour selection strategies for POI recommendation in LBSNs; Sage Publications Ltd; Journal Of Information Science; 44; 6; 5-3-2018; 802-817 0165-5515 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/91004 |
identifier_str_mv |
Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis; A study of neighbour selection strategies for POI recommendation in LBSNs; Sage Publications Ltd; Journal Of Information Science; 44; 6; 5-3-2018; 802-817 0165-5515 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://journals.sagepub.com/doi/10.1177/0165551518761000 info:eu-repo/semantics/altIdentifier/doi/10.1177%2F0165551518761000 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR) https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR) https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Sage Publications Ltd |
publisher.none.fl_str_mv |
Sage Publications Ltd |
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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1844613378005270529 |
score |
13.070432 |