Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation

Autores
Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Location-based recommender systems (LBRSs) provide a technological solution for helping users to cope with the vast amount of information coming from geo-localization services. Most online social networks capture the geographic location of users and their points-of-interests (POIs). Location-based social networks (LBSNs), like Foursquare, leverage technologies such as GPS, Web 2.0 and smartphones allow users to share their locations (check-ins), search for POIs, look for discounts, comment about specific places, connect with friends and find the ones who are near a specific location. LBRSs play an important role in social networks nowadays as they generate suggestions based on techniques such as collaborative filtering (CF). In this traditional recommendation approach, prediction about a user preferences are based on the opinions of like-minded people. Users that can provide valuable information for prediction need to be first selected from the complete network and, then, their opinions weighted according to their expected contribution. In this paper, we propose and analyze a number of strategies for selecting neighbors within the CF framework leveraging on information contained in the users' social network, common visits, visiting area and POIs categories as influential factors. Experimental evaluation with data from Foursquare social network shed some light on the impact of different mechanisms on user weighting for prediction.
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
COLLABORATIVE FILTERING
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:11336/59727
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Selecting and Weighting Users in Collaborative Filtering-based POI RecommendationRios, CarlosSchiaffino, Silvia NoemiGodoy, Daniela LisLOCATION-BASED SOCIAL NETWORKSRECOMMENDER SYSTEMSCOLLABORATIVE FILTERINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Location-based recommender systems (LBRSs) provide a technological solution for helping users to cope with the vast amount of information coming from geo-localization services. Most online social networks capture the geographic location of users and their points-of-interests (POIs). Location-based social networks (LBSNs), like Foursquare, leverage technologies such as GPS, Web 2.0 and smartphones allow users to share their locations (check-ins), search for POIs, look for discounts, comment about specific places, connect with friends and find the ones who are near a specific location. LBRSs play an important role in social networks nowadays as they generate suggestions based on techniques such as collaborative filtering (CF). In this traditional recommendation approach, prediction about a user preferences are based on the opinions of like-minded people. Users that can provide valuable information for prediction need to be first selected from the complete network and, then, their opinions weighted according to their expected contribution. In this paper, we propose and analyze a number of strategies for selecting neighbors within the CF framework leveraging on information contained in the users' social network, common visits, visiting area and POIs categories as influential factors. Experimental evaluation with data from Foursquare social network shed some light on the impact of different mechanisms on user weighting for prediction.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; ArgentinaBudapest Tech2017-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59727Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis; Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation; Budapest Tech; Acta Polytechnica Hungarica; 14; 3; 2-2017; 13-321785-8860CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.uni-obuda.hu/journal/Rios_Schiaffino_Godoy_74.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:19:32Zoai:ri.conicet.gov.ar:11336/59727instacron: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 10:19:32.543CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
title Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
spellingShingle Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
Rios, Carlos
LOCATION-BASED SOCIAL NETWORKS
RECOMMENDER SYSTEMS
COLLABORATIVE FILTERING
title_short Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
title_full Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
title_fullStr Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
title_full_unstemmed Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
title_sort Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
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
COLLABORATIVE FILTERING
topic LOCATION-BASED SOCIAL NETWORKS
RECOMMENDER SYSTEMS
COLLABORATIVE FILTERING
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) provide a technological solution for helping users to cope with the vast amount of information coming from geo-localization services. Most online social networks capture the geographic location of users and their points-of-interests (POIs). Location-based social networks (LBSNs), like Foursquare, leverage technologies such as GPS, Web 2.0 and smartphones allow users to share their locations (check-ins), search for POIs, look for discounts, comment about specific places, connect with friends and find the ones who are near a specific location. LBRSs play an important role in social networks nowadays as they generate suggestions based on techniques such as collaborative filtering (CF). In this traditional recommendation approach, prediction about a user preferences are based on the opinions of like-minded people. Users that can provide valuable information for prediction need to be first selected from the complete network and, then, their opinions weighted according to their expected contribution. In this paper, we propose and analyze a number of strategies for selecting neighbors within the CF framework leveraging on information contained in the users' social network, common visits, visiting area and POIs categories as influential factors. Experimental evaluation with data from Foursquare social network shed some light on the impact of different mechanisms on user weighting for prediction.
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) provide a technological solution for helping users to cope with the vast amount of information coming from geo-localization services. Most online social networks capture the geographic location of users and their points-of-interests (POIs). Location-based social networks (LBSNs), like Foursquare, leverage technologies such as GPS, Web 2.0 and smartphones allow users to share their locations (check-ins), search for POIs, look for discounts, comment about specific places, connect with friends and find the ones who are near a specific location. LBRSs play an important role in social networks nowadays as they generate suggestions based on techniques such as collaborative filtering (CF). In this traditional recommendation approach, prediction about a user preferences are based on the opinions of like-minded people. Users that can provide valuable information for prediction need to be first selected from the complete network and, then, their opinions weighted according to their expected contribution. In this paper, we propose and analyze a number of strategies for selecting neighbors within the CF framework leveraging on information contained in the users' social network, common visits, visiting area and POIs categories as influential factors. Experimental evaluation with data from Foursquare social network shed some light on the impact of different mechanisms on user weighting for prediction.
publishDate 2017
dc.date.none.fl_str_mv 2017-02
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/11336/59727
Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis; Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation; Budapest Tech; Acta Polytechnica Hungarica; 14; 3; 2-2017; 13-32
1785-8860
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59727
identifier_str_mv Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis; Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation; Budapest Tech; Acta Polytechnica Hungarica; 14; 3; 2-2017; 13-32
1785-8860
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://www.uni-obuda.hu/journal/Rios_Schiaffino_Godoy_74.pdf
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
dc.publisher.none.fl_str_mv Budapest Tech
publisher.none.fl_str_mv Budapest Tech
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 13.070432

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