The use of big data in adaptive gamification in collaborative location collecting systems: a case of traveling behavior detection
The use of big data in adaptive gamification in collaborative location collecting systems: a case of traveling behavior detection
| cic.institucionOrigen | Laboratorio de Investigación y Formación en Informática Avanzada (LIFIA) | es |
| cic.isFulltext | true | es |
| cic.isPeerReviewed | true | es |
| cic.lugarDesarrollo | Laboratorio de Investigación y Formación en Informática Avanzada | es |
| cic.version | info:eu-repo/semantics/submittedVersion | es |
| dc.date.accessioned | 2022-02-14T12:23:13Z | |
| dc.date.available | 2022-02-14T12:23:13Z | |
| dc.identifier.uri | https://digital.cic.gba.gob.ar/handle/11746/11425 | |
| dc.title | The use of big data in adaptive gamification in collaborative location collecting systems: a case of traveling behavior detection | en |
| dc.type | Artículo | es |
| dcterms.abstract | Collaborative 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.author | Dalponte Ayastuy, María | es |
| dcterms.creator.author | Torres, Diego | es |
| dcterms.extent | 1-15 | es |
| dcterms.isPartOf.item | The use of big data in adaptive gamification in CLCS | es |
| dcterms.issued | 2021 | |
| dcterms.language | Inglés | es |
| dcterms.license | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | es |
| dcterms.subject | Adaptive gamification challenges | en |
| dcterms.subject | Spatial-temporal user profiling | en |
| dcterms.subject | Users behavioural patterns | en |
| dcterms.subject.materia | Ciencias de la Computación | es |
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