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Relevance of non-activity representation in traveling user behavior profiling for adaptive gamification

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Collaborative location collecting systems (CLCS) are collaborative systems where users collects location-based data. When these systems are gamified and aim to adapt the game elements to each user, it may require a user traveling behavior profile. This work presents two approaches of traveling user behavior profiling: a raw series built up with categorical data that describes the user’s activity in a period, and a timed series that is an enhanced version of the first that includes a representation of the non-activity time frames. The profiling of user traveling behavior can be used in adaptive gamification strategies. The approach is evaluated over a behavioral atoms dataset based on a year of Foursquare check-ins. The results showed that both approaches reflex different aspects of traveling user behavior, and also both could be used in a complementary manner.

Palabras clave
User profiling
Adaptive gamification
Collaborative location collecting systems
Dynamic time warping clustering

Esta obra se publica con la licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (BY-NC-SA 4.0)
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