Prof. Kobi Gal

Online systems utilize user data, such as demographics, past performance, preferences and skillset to construct an accurate model of users and maximize personalization. Some of these user features are “shallow” traits which seldom change (e.g. age, race, gender) while others are “deep” traits that are more volatile (e.g. performance, goals, interests). In this work, we explore how reasoning about this diversity of user features can enhance performance of personalized systems. By modeling the personalization process as a Reinforcement Learning (RL) problem, we introduce Diversity Aware Bandits for Intervention Personaliztion (DABIP), a novel contextual multi-armed bandit algorithm that leverages the dynamics within user features to cluster users while maximizing outcomes. We demonstrate the efficacy of this approach using two real world datasets from different domains.