An Overview of Federated Recommendation

PhD Qualifying Examination


Title: "An Overview of Federated Recommendation"

by

Mr. Liu YANG


Abstract:

Recommender systems are heavily data-driven. In general, the more data the 
recommender systems use, the better the recommendation results are. However, 
due to privacy and security constraints, directly sharing user data is 
undesired. Such decentralized silo issues commonly exist in recommender 
systems. There have been many pilot studies on protecting data privacy and 
security when utilizing data silos. But, most works still need the users' 
private data to leave the local data repository. Federated learning is an 
emerging technology that tries to bridge the data silos and build machine 
learning models without compromising user privacy and data security. In this 
paper, we introduce a new notion of federated recommender systems, an 
instantiation of federated learning on decentralized recommendation. We 
formally define the problem of the federated recommender systems. Then, we 
focus on categorizing and reviewing the current approaches from the perspective 
of the federated learning. Finally, we put forward several promising future 
research challenges and directions.


Date:			Friday, 24 July 2020

Time:                  	4:00pm - 6:00pm

Zoom meeting:           https://hkust.zoom.us/j/91865239782

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Dr. Kai Chen (Supervisor)
 			Dr. Yangqiu Song (Chairperson)
 			Dr. Qifeng Chen


**** ALL are Welcome ****