*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Talk Title:
User-centric Recommendation Models and Systems
Talk Abstract:
The daily actions and decisions of people are increasingly shaped by recommendation systems, from e-commerce and content platforms to education and wellness applications. These systems selectively suggest and present information items based on their characterization of user preferences. However, existing preference modeling methods are limited due to the incomplete and biased nature of the behavioral data that inform the models. As a result, recommendations can be narrow, skewed, homogeneous, and divergent from users’ aspirations.
In this talk, I will introduce user-centric recommendation models and systems that address the incompleteness and bias of existing methods and increase systems’ utility for individuals. Specifically, I will present my work addressing two key research challenges: (1) inferring debiased preferences from biased behavioral data using counterfactual reasoning, and (2) eliciting unobservable current and aspirational preferences from users through interactive machine learning. I will conclude with discussion of field experiments that demonstrate how user-centric systems can promote healthier diets and better content choices.
Your Bio:
Longqi Yang is a Computer Science Ph.D. candidate at Cornell University, advised by Prof. Deborah Estrin. He conducted his thesis research as a part of the Connected Experiences (Cx) lab and the Small Data Lab, both at Cornell Tech. His research is focused on personalization, recommendation systems, and machine learning for user behavior modeling. In this line of work, Longqi makes various types of contributions including developing novel recommendation methods and algorithms using advanced machine learning and deep learning techniques, building deployable systems, and conducting lab and field experiments. His work has been deployed and adopted commercially and recognized in flagship industrial and academic conferences. He received his B.Eng. in Information and Communication Engineering from Zhejiang University in 2014.
