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...
| Authors: | , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2022 |
| Country: | Argentina |
| Institution: | Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
| Repository: | CIC Digital (CICBA) |
| Language: | English |
| OAI Identifier: | oai:digital.cic.gba.gob.ar:11746/11560 |
| Online Access: | https://digital.cic.gba.gob.ar/handle/11746/11560 |
| Access Level: | Open access |
| Keyword: | Ciencias de la Computación e Información Adaptive gamification challenges Spatial-temporal user profiling Users behavioural patterns |
| Summary: | 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 spatialtemporal 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 two types of behavioral atoms and two types of users’ behavioral patterns. |
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