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SIS Research Seminar by Xiangnan HE | Neural Recommender Models: Moving from Shallow Learning to Deep Learning

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Neural Recommender Models:
Moving from Shallow Learning to Deep Learning

Speaker (s):

Dr. Xiangnan HE
Senior Research Fellow,
School of Computing,
National University of Singapore

 

Date:

Time:

Venue:

 

April 13, 2018, Friday

1:30pm - 3:00pm

Meeting Room 5.1, Level 5
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902

 

 

ABSTRACT

Recommendation systems play a vital role in online information systems and are a major monetization tool for user-oriented platforms. In recent years, there has been increasing research interest in recommendation technologies in both industry and academia, and significant progress has been made owing to the fast development of neural network techniques. In this talk, I will summarize our several contributions in developing neural recommender models published in SIGIR, WWW and IJCAI recently. First, I will introduce the neural collaborative filtering (CF) methods, including neural matrix factorization, attentive CF, and deep convolutional CF. Second, I will present the generic feature-based methods, including neural factorization machine (FM), attentional FM, and tree-enhanced embedding model.
 

About the Speaker

Dr. Xiangnan HE is a senior research fellow with School of Computing, National University of Singapore (NUS). He received his Ph.D. in Computer Science from NUS. His research interests span recommender systems, information retrieval, and multi-media processing. He has over 30 publications appeared in several top conferences such as SIGIR, WWW, MM, and IJCAI, and journals including TKDE, TOIS, and TMM. His work on recommender systems has received the Best Paper Award Honourable Mention of ACM SIGIR 2016. Moreover, he has served as the PC member for several top conferences including SIGIR, WWW, MM, KDD, ACL, IJCAI etc, and the regular reviewer for journals including TKDE, TOIS, TKDD, TMM etc.