Craig Boutilier, Google

Abstract

[Presentation video]

Recommender systems (RSs) lie at the center of complex ecosystems, involving large numbers of users, content providers or vendors, advertisers and even competing platforms, whose behaviors are driven by their incentives or preferences for RS-induced outcomes. The resulting interactions can generate complex dynamics which, in turn, impacts the ability of the RS to act in the best interests of any particular actor or implement tradeoffs w.r.t. the interests of different actors. The design of RSs in such settings has received relatively scant attention. We briefly illustrate examples of such interactions and discuss the use of mechanism design (MD)—and—adjacent areas, such as preference elicitation, behavioral economics, reinforcement learning, etc.—as a means to ensure RSs have positive societal impact. We also identify a number of research challenges that must be addressed to bring MD to bear on recommender ecosystems.

Speaker Bio: Craig Boutilier is Principal Scientist at Google. He works on various aspects of decision making under uncertainty, with a current focus on recommender systems (and ecosystems), user modeling, reinforcement learning, preference elicitation, and related topics.

He was a Professor in the Department of Computer Science at the University of Toronto (on leave) and Canada Research Chair in Adaptive Decision Making for Intelligent Systems. He received his Ph.D. in Computer Science from the University of Toronto in 1992, and worked as an Assistant and Associate Professor at the University of British Columbia from 1991 until his return to Toronto in 1999. He served as Chair of the Department of Computer Science at Toronto from 2004-2010. He was co-founder (with Tyler Lu) of Granata Decision Systems from 2012-2015, until his move to Google in 2015.

Boutilier was a consulting professor at Stanford University from 1998-2000, an adjunct professor at the University of British Columbia from 1999-2010, and a visiting professor at Brown University in 1998, at the University of Toronto in 1997-98, at Carnegie Mellon University in 2008-09, and at Université Paris-Dauphine (Paris IX) in the spring of 2011. He served on the Technical Advisory Board of CombineNet, Inc. from 2001 to 2010.

Boutilier's research interests have spanned a wide range of topics, from knowledge representation, belief revision, default reasoning, and philosophical logic, to probabilistic reasoning, decision making under uncertainty, multiagent systems, and machine learning. His current research efforts focus on various aspects of decision making under uncertainty: preference elicitation, mechanism design, game theory and multiagent decision processes, economic models, social choice, computational advertising, Markov decision processes, reinforcement learning and probabilistic inference.

Boutilier is a past Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). He was a past Associate Editor with the ACM Transactions on Economics and Computation (TEAC), the Journal of Artificial Intelligence Research (JAIR), the Journal of Machine Learning Research (JMLR), and Autonomous Agents and Multiagent Systems (AAMAS); and he has sat on the editorial/advisory boards of several other journals. Boutilier has organized several international conferences and workshops, including his work as Program Chair of the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09) and Program Chair of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-2000). He has also served on the conference program committees of roughly 60 leading international conferences.

Boutilier is a Fellow of the Royal Society of Canada (RSC), the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI). He was the recipient of the 2018 ACM/SIGAI Autonomous Agents Research Award, He was awarded a Tier Ir Canada Research Chair, an Isaac Walton Killam Research Fellowship, and an IBM Faculty Award. He received the Killam Teaching Award from the University of British Columbia in 1997. He has also received a number of Best Paper awards including: the 2009 IJCAI-JAIR Best Paper Prize (with R. Brafman, C. Domshlak, H. Hoos, D. Poole, from the Journal of Artificial Intelligence Research); the 2014 AIJ Prominent Paper Award (with S. Sanner, from the journal Artificial Intelligence); the 2018 NeurIPS Best Paper Award (w. T. Lu, D. Schuurmans); and the 2022 AIJ Prominent Paper Award (with I. Caragiannis, S. Haber, T. Lu, A. Procaccia and O. Sheffet, from the journal Artificial Intelligence).