The use of big data in adaptive gamification in collaborative location collecting systems: a case of traveling behavior detection

cic.institucionOrigenLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)es
cic.isFulltexttruees
cic.isPeerReviewedtruees
cic.lugarDesarrolloLaboratorio de Investigación y Formación en Informática Avanzadaes
cic.versioninfo:eu-repo/semantics/submittedVersiones
dc.date.accessioned2022-02-14T12:23:13Z
dc.date.available2022-02-14T12:23:13Z
dc.identifier.urihttps://digital.cic.gba.gob.ar/handle/11746/11425
dc.titleThe use of big data in adaptive gamification in collaborative location collecting systems: a case of traveling behavior detectionen
dc.typeArtículoes
dcterms.abstractCollaborative location collecting systems (CLCS) is a particular case of collaborative systems where a community of users collaboratively collects data associated with a geo-referenced location. Gamification is a strategy to convene participants to CLCS. However, it cannot be generalized because of the different users’ profiles, and so it must be tailored to the users and playing contexts. A strategy for adapting gamification in CLCS is to build game challenges tailored to the player’s spatio-temporal behavior. This type of adaptation requires having a user traveling behavior profile. Particularly, this work is focused on the first steps to detect users’ behavioral profiles related to spatial-temporal activities in the context of CLCS. Specifically, this article introduces: (1) a strategy to detect patterns of spatial-temporal activities, (2) a model to describe the spatial-temporal behavior of users based on (1), and a strategy to detect users’ behavioral patterns based on unsupervised clustering. The approach is evaluated over a Foursquare dataset. The results showed four types of behavioral atoms and nine types of users’ behavioral patterns.en
dcterms.creator.authorDalponte Ayastuy, Maríaes
dcterms.creator.authorTorres, Diegoes
dcterms.extent1-15es
dcterms.isPartOf.itemThe use of big data in adaptive gamification in CLCSes
dcterms.issued2021
dcterms.languageIngléses
dcterms.licenseAttribution-NonCommercial-NoDerivatives 4.0 Internacionales
dcterms.subjectAdaptive gamification challengesen
dcterms.subjectSpatial-temporal user profilingen
dcterms.subjectUsers behavioural patternsen
dcterms.subject.materiaCiencias de la Computaciónes
Archivos
Paquete original
Mostrando1 - 1 de 1
Imagen en miniatura
Nombre:
Dalponte Torres - The use of big data in adaptive gamification in collaborative location collecting.pdf-PDFA.pdf
Tamaño:
2.16 MB
Formato:
Adobe Portable Document Format
Descripción:
Documento completo