Personalized Game Content Generation and Recommendation for Gamified Systems
We have developed a software framework that enables the gamification of various aspects of citizens’ life in a Smart City, for instance, in sustainable urban mobility. While gamification is often effective in inducing behavioral changes in citizens, well-known limitations concern retaining players, and sustaining over time the new behaviors promoted through gamification. Our hypothesis is that we can overcome these limitations by providing a variety of playable units that make the game experience of involved citizens more varied, and at the same time more compelling, since they are personalized to each individual player. To this end, as a part of our gamification approach we have designed and implemented a framework for the automatic generation and recommendation of challenges, which are personalized and contextualized based on the preferences, history, game status and performance of each player. In addition, we built up the proposed framework by exploiting the Machine Learning and Player Modeling modules to optimize challenge selection process and modeling the players play styles, respectively. In this seminar I will describe our approach in details, present the evaluation results on a smart mobility game that involved the proposal of weekly challenges to hundreds of citizen-players, and discuss some on-going and future works.