Toward Understanding Users' Behaviors in Multi-Armed Bandits

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Abstract:
Multi-Armed bandits have proven to be an effective tool in many real-world applications, such as recommender systems, online advertising and healthcare. In these applications, the ultimate goal is to satisfy the users' needs. Therefore, in this talk, I will introduce our recent work on understanding users' behaviors in multi-armed bandits. More specifically, I will focus on three unique angles, namely to understand the mutual influence among users and items via local clustering and graph neural networks, to understand users’ multifaceted needs via multifaceted contextual bandits, and to understand the balance between exploitation and exploration with multiple neural networks. Towards the end, I will also share my thoughts regarding interesting future directions.

Speaker bio:

Dr. Jingrui He is an associate professor at the School of Information Sciences, University of Illinois at Urbana-Champaign. She received her PhD from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, rare category analysis, active learning and semi-supervised learning, with applications in security, social network analysis, health care and manufacturing processes. Dr. He is the recipient of the 2016 NSF CAREER Award, the 2020 OAT Award, three times recipient of the IBM Faculty Award in 2018, 2015 and 2014 respectively, and was selected as IJCAI 2017 Early Career Spotlight. Dr. He has more than 130 publications at major conferences (e.g., IJCAI, AAAI, KDD, ICML, NeurIPS) and journals (e.g., TKDE, TKDD, DMKD), and is the author of two books. Her papers have received the Distinguished Paper Award at FAccT 2022, as well as Bests of the Conference at ICDM 2016, ICDM 2010 and SDM 2010.