An important goal for recommender systems is to make recommendations that maximize some form of user utility over (ideally, extended periods of) time. While reinforcement learning has started to find limited application in recommendation settings, for the most part, practical recommender systems remain "myopic" (i.e., focused on immediate user responses). Moreover, they are "local" in the sense that they rarely consider the impact that a recommendation made to one user may have on the ability to serve other users. These latter "ecosystem effects" play a critical role in optimizing long-term user utility. In this talk, I describe some recent work we have been doing to optimize user utility and social welfare using reinforcement learning and equilibrium modeling of the recommender ecosystem; draw connections between these models and notions such as fairness and incentive design; and outline some future challenges for the community.
Craig Boutilier is a Principal Scientist at Google. He received his Ph.D. in Computer Science from U. Toronto (1992), and has held positions at U. British Columbia and U. Toronto (where he served as Chair of the Dept. of Computer Science). He co-founded Granata Decision Systems, served as a technical advisor for CombineNet, Inc., and has held consulting/visiting professor appointments at Stanford, Brown, CMU and Paris-Dauphine. Boutilier's current research focuses on various aspects of decision making under uncertainty, including: recommender systems; user modeling; MDPs, reinforcement learning and bandits; preference modeling and elicitation; mechanism design, game theory and multi-agent decision processes; and related areas. Past research has also dealt with: knowledge representation, belief revision, default reasoning and modal logic; probabilistic reasoning and graphical models; multi-agent systems; and social choice. Boutilier served as Program Chair for IJCAI-09 and UAI-2000, and as Editor-in-Chief of the Journal of AI Research (JAIR). He is a Fellow of the Royal Society of Canada (FRSC), the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI). He also received the 2018 ACM/SIGAI Autonomous Agents Research Award.