Title: Exploiting Human Trust to Improve Recommender Systems

Abstract:

Recommender systems based on collaborative filtering suffer from the problems of data sparsity and cold start. Trust-aware recommender systems exploit human trust relationships to improve recommendation performance. In this talk, I will briefly summarize the state-of-the-art methods along this line of research and highlight a few representative works from our group. I will also point out several remaining challenges and interesting future directions.

Speaker: Jie Zhang

Date/Time: 4:00-5:00pm, 9 June, 2022

Online Platform: Tencent Meeting ID: 911-7495-8724

Brief introduction of the lecturer: 

Jie Zhang, received his bachelor degree of computer science from Nanjing University of Aeronautics and Astronautics, and received his doctorate of computer science from University of Waterloo, Canada. He is currently an associate professor at School of Computer Science and Engineering, Nanyang Technological University, Singapore. His main research field is a sub field of artificial intelligence - user modeling, which mainly studies trust modeling and preference modeling in various emerging application fields. His papers were published on top AI conferences (such as NeurIPS, AAAI and IJCAI) and top journals (such as TKDE and AIJ). He has won the best paper award at conferences for many times, as well as the world's top 2% Scientist Award and AI 2000 most influential scholar award issued by Stanford University. He is also senior editor of Electronic Commerce Research and Applications, and will serve as the general chairman of ACM Recommender Systems Conference in 2023.