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dc.contributor.author
Rios, Carlos
dc.contributor.author
Schiaffino, Silvia Noemi
dc.contributor.author
Godoy, Daniela Lis
dc.date.available
2018-09-14T18:17:24Z
dc.date.issued
2017-02
dc.identifier.citation
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
dc.identifier.issn
1785-8860
dc.identifier.uri
http://hdl.handle.net/11336/59727
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Budapest Tech
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Location-Based Social Networks
dc.subject
Recommender Systems
dc.subject
Collaborative Filtering
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Selecting and Weighting Users in Collaborative Filtering-based POI Recommendation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-09-05T15:52:25Z
dc.journal.volume
14
dc.journal.number
3
dc.journal.pagination
13-32
dc.journal.pais
Hungría
dc.description.fil
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
dc.description.fil
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
dc.description.fil
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
dc.journal.title
Acta Polytechnica Hungarica
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.uni-obuda.hu/journal/Rios_Schiaffino_Godoy_74.pdf
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