CS SEMINAR #2: An Introduction to Neural Recommender Models

CS SEMINAR #2: An Introduction to Neural Recommender Models

Date & Time
Sunday, 6 January, 2019 - 15:30 to 17:30
Food and beverages served?
Seminar room
Type of event
Public Event (Open for walk-ins)

Recommendation systems play a vital role in online information systems and are a major monetisation tool for user-oriented platforms. Recently, there has been an increasing research interest in recommendation technology, and significant progress has been made owing to the fast development of neural network techniques.

In this talk, Dr. He will briefly introduce traditional recommendation techniques and emphasise on the emerging neural recommender models. He will cover several advances on feature representation learning models, feature interaction learning models, and adversarial training method.

Dr He Xiangnan speaker bio

Dr. He Xiangnan is a senior research fellow at School of Computing, National University of Singapore. 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 50 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 WWW 2018 and SIGIR 2016. Moreover, he has served as the PC member for several top conferences including SIGIR, WWW, MM, KDD etc., and the regular reviewer for journals including TKDE, TOIS etc.

6Estates is an AI-driven intelligence company. We specialise in Natural Language Processing, Deep Learning, and Knowledge Graph to deliver real-time, actionable insights to businesses, with a focus on Market Intelligence and Finance Intelligence.

CS Seminar is an initiative by 6Estates to facilitate the sharing of technical knowledge by academics and industry experts, to promote AI & Computer Science community in Singapore.

The venue for CS Seminar is sponsored by BLOCK71.

For any enquiries, kindly contact: [email protected] / +65 6777 2757