The use of big data in adaptive gamification in collaborative location collecting systems: a case of traveling behavior detection
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...
| Autores: | , |
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| Formato: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2021 |
| País: | Argentina |
| Recursos: | Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
| Repositorio: | CIC Digital (CICBA) |
| Idioma: | inglés |
| OAI Identifier: | oai:digital.cic.gba.gob.ar:11746/11425 |
| Acesso em linha: | https://digital.cic.gba.gob.ar/handle/11746/11425 |
| Access Level: | acceso abierto |
| Palavra-chave: | Ciencias de la Computación Adaptive gamification challenges Spatial-temporal user profiling Users behavioural patterns |
| Resumo: | 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. |
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