The enormous scale of the intangible economy has created much better services and countless choices for Internet users. Online stores, without shelf space constraints, could offer their customers direct access to huge warehouses. Netflix could offer its members quality content to consume as much as they may have time to watch. A key point to a successful user experience is an effective way for users to explore and find what they want, which could be something that users do not know yet. I believe that recommendations systems that can instantly incorporate and response to subtle changes in a user action either implicit or explicit, would be critical. In this talk, I will discuss some challenges and approaches to designing these machine learning models.
Trung is a Research Scientist at Netflix, where his main focus is to advance the search and recommendation systems serving hundreds of millions of users. He is interested in self-supervised learning methods, deep reinforcement learning, and imitation learning. Prior to Netflix, Trung was a lead researcher at Adobe working on display advertising and conversational bot. Trung holds a PhD from National University of Singapore where he researched and published work on model-based reinforcement learning.