Venue: Room 206
Date: Monday, August 15th, 2022


D.C. Time (EDT, UTC-4:00) Event Chair
8:30am-8:40am Opening Remarks   Ninghao Liu
8:40am-9:25am Keynote 1
Speaker: James Caverlee, Texas A&M University, USA
Talk Title: Interaction Modeling and User Fairness in Recommendation 		Ninghao Liu
9:25am-9:40am Coffee Break
9:40am-10:35am Paper Presentation Session 1 (14' *4 papers)
#1073 (in person): FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR

#4737 (in person): On the Relationship between Counterfactual Explainer and Recommender: A Framework and Preliminary Observations

#2069 (recording): Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks

#1159 (recording): Modeling Multi-interest News Sequence for News Recommendation 		Ninghao Liu
10:35am-11:20am Keynote 2
Speaker: Xia (Ben) Hu, Rice University, USA
Talk Title: Bridging Interpretation and Fairness in Machine Learning 		Mengdi Huai
11:20am-12:05pm Keynote 3
Speaker: Alex Beutel, Google Research, USA
Talk Title: Understanding and Improving Recommenders for All 		Yushun Dong
12:05pm-13:00pm Lunch
13:00pm-13:45pm Keynote 4
Speaker: Fei Fang, Carnegie Mellon University, USA
Talk Title: A Recommender System for Crowdsourcing Food Rescue Platforms 		Jing Ma
13:45pm-15:00pm Paper Presentation Session 2 (14' *5 papers)
#6310 (in person): Evolution of Popularity Bias: Empirical Study and Debiasing

#8964 (in person): On Curating Responsible and Representative Healthcare Video Recommendations for Patient Education and Health Literacy: An Augmented Intelligence Approach

#2990 (recording): An Approach to Ensure Fairness in News Articles

#6466 (recording): Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX

#3016 (TBA): MAFD: A Federated Distillation Approach with Multi-head Attention for Recommendation Task 		Ninghao Liu
15:00pm-15:20pm Coffee Break
15:20pm-16:05pm Keynote 5
Speaker: Sheng Li, University of Virginia, USA
Talk Title: Towards Trustworthy Representation Learning 		Mengdi Huai
16:05pm-16:50pm Keynote 6
Speaker: Tie Wang, LinkedIn Data and AI foundation, USA
Talk Title: Practical Paths to Data Driven Recommendation System 		Ninghao Liu
16:50pm-17:00pm Award Ceremony&Closing Remarks
 Ninghao Liu

Note: Each paper presentation includes 12mins presentation plus 2mins QA.

Important details for speakers and presenters:

  • All presenters must use their own laptop for their presentation.
  • The connection to the projector is HDMI.
  • There will be dedicated conference wifi in all meeting rooms for presenters and attendees to use.
  • If a presenter is playing a video, we recommend that they download the video to their device prior to their presentation.
  • There are limited outlets in the meeting rooms and conference space to charge your laptop, presenters must ensure their laptops are fully charged before their session.
  • There will be AV tech staff and Executive Events Planning support checking on all rooms 30 minutes before the session begins, to assist presenters with any technical issues.
  • For each workshop, we have assigned a student volunteer to help you organize your workshop. The volunteer will show up 30 minutes before the session begins.