DS/CSS Seminar: Ed Chi
Building and Understanding Recommenders for Long-Term User Experiences
Abstract:
How can and should recommender systems shape user experiences? In recent years, our understanding of the objective of recommenders has evolved from making individual good predictions of user interest, to now creating positive, long-term experiences. This task of enabling positive, long-term experiences is significantly more challenging, both making clear the recommender responsibility in shaping the user experience and the difficulty in optimizing over long-term user trajectories.
In this talk we'll discuss three recent advances in understanding how recommenders effect the user experience and reinforcement learning can be used to improve it: (1) We'll start with how traditional recommendation can be framed as an RL task and how off-policy RL can significantly improve user satisfaction. (2) We'll explore what are the effects of the recommender on the user experience using simulations. (3) We'll discuss how we can control for some effects through framing the recommendation challenge as a multi-objective safe RL problem. Finally, I'll comment on how recommenders are dynamic wicked problems that are socially complex with no stopping points. It is hard or impossible to summarize modeling objectives into loss functions as boundary objects, which implies what we need are learning objects that help us archive and make sense of the evolving problem.
Speaker Bio:
Ed H. Chi is a Principal Scientist at Google, leading several machine learning research teams focusing on neural modeling, inclusive ML, reinforcement learning, and recommendation systems in Google Brain. He has delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >170 product launches in the last 3 years. With 39 patents and over 120 research articles, he is also known for research on user behavior in web and social media.
Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group, where he led the team in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he recently received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.
The University of Michigan Data Science / Computational Social Science faculty host a seminar series that features invited talks, research presentations and informal work-in-progress discussions. A list of the scheduled speakers for Winter 2021 (January-April) is available here.