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Machine Learning for Personalized Challenges in a Gamified Sustainable Mobility Scenario

Gamification has been successfully applied in several application domains to persuade people to reach to a certain goal. However, such persuasive technologies have concerns to keep players engaged and to sustain newly acquired behaviors for a long term. One option to tackle this issue is personalizing the gamified experience for each individual player. To this end, we developed a framework that can personalize the game content by proposing to players challenges that are tailored to their profiles and game histories. In this paper we present a Machine Learning module that optimizes the challenge selection process and the evaluation results within an on-the-field game promoting sustainable mobility habits.