Toward User-Centric Recommender Systems

Artificial intelligence and machine learning technologies continue to broaden and influence our access to information, entertainment, products and services-and each other-through data-driven recommendations. While the increased access afforded by AI has undoubtedly improved certain aspects of social welfare, the ability of recommenders to generate genuinely personalized recommendations and engage users in meaningful ways remains limited. Furthermore, our understanding of how AI recommenders shape long-term user behavior is poorly understood. In this talk, I will discuss the role that various AI techniques have to play in next-generation, user-centric recommender systems. Among these are preference modeling and preference elicitation; reinforcement learning and latent state models; behavioral decision theory and economics; and modeling of user behavioral preferences. I will also highlight challenges that emerge when putting these methods into practice.