Universidad Nacional de Quilmes

Fits Like a Game: A Multi-criteria Adaptive Gamification for Collaborative Location-Based Collecting Systems

Collaborative location-based collecting systems often struggle with engagement because static, uniform gamification strategies fail to account for diverse user motivations and project requirements.

• A new adaptive gamification framework achieves a 0.9 precision rate in predicting effective game elements for location-based collecting systems.
• The system replaces static, one-size-fits-all gamification with a multi-criteria recommendation model that balances individual user preferences with broader project data collection goals.
• Technical architecture utilizes K-means clustering on behavioral atoms to generate user traveling behavior time series, enabling accurate collaborative filtering.