Exploration – Exploitation Dilemma in Recommender Systems: Is It the Time to Switch to the Exploration Mode Again?

Machine Learning (ML) became the predominate research paradigm in Recommender Systems over the last several years leading to many fundamental advances in the field, both in the academia and in the industry. Unfortunately, these achievements were accomplished as a result of shifting focus from other research paradigms towards ML. In this talk, I will argue that it is crucial for the Recommender Systems field to broaden its scope of inquiry by enhancing its focus on other disciplines, such as psychology and marketing, sociology and social science, management and organizational behavior, economics, decision science and other disciplines that have been underexplored in Recommender Systems. By shifting the focus of inquiry towards the “exploration” component of the explore-exploit paradigm, we can potentially achieve new significant breakthroughs in recommender systems. I will present some case studies supporting this claim.

Alexander Tuzhilin is Leonard N. Stern Professor in the Department of Technology, Operations and Statistics at the Stern School of Business, NYU. His research interests include AI, personalization, recommender systems and machine learning, and he published widely on these and related topics. Professor Tuzhilin has served on the organizing committees of numerous conferences, including as the Program and also the General Chair of the IEEE International Conference on Data Mining (ICDM), and as the Program Chair, the Conference Chair and the Chair of the Steering Committee of the ACM Conference on Recommender Systems (RecSys). He served on the editorial boards of several journals, including as the Editor-in-Chief of the ACM Transactions on Management Information Systems. His past doctoral students have joined leading universities, including the Wharton School at the University of Pennsylvania, Georgia Institute of Technology, University of Minnesota, Boston University, Emory University and other schools. He is a recipient of the INFORMS Information Systems Society Design Science Award and is a Distinguished Fellow of the ISS IFORMS Society. He received Ph.D. in Computer Science from the Courant Institute of Mathematical Sciences, NYU.