Tutorial 5. Tuesday, June 11th 14:00-15:30 (#104, 1F)

Exploiting Side Information for Recommendation
Presentation Material

by Qing Guo, Zhu Sun and Yin Leng Theng (Nanyang Technological University, Singapore)

Short Bios :

Qing Guo is a Ph.D. candidate in Wee Kim Wee School of Communication and Information at Nanyang Technological University. He focuses on Point-of-Interest (POI) recommendation by exploiting the heterogeneous information in location-based social networks. He obtained his M.Sc. in The University of Hong Kong in 2014 and B.E. from University of Electronic Science and Technology of China in 2013. While doing Ph.D. study, he was also a research associate in SAP Innovation Center network from 2015 to 2018, where he participated in machine learning products development in SAP products. Now, he is a data scientist in Shopee Singapore and continue to work on recommendation research and applications.

Zhu Sun obtained her Ph.D. Degree from School of Computer Science and Engineering, Nanyang Technological University, Singapore, in 2018. During Ph.D. study, she focused on design efficient recommendation algorithms to improve the performance of recommender systems. Her research has been published in leading conferences and journals in related domains (e.g., ACM UMAP, ACM RecSys, IJCAI, AAAI, CIKM). Zhu Sun is active in serving research communities. She is the co-chair for the international workshop on Recommender Systems for Citizens (CitRec) at ACM RecSys 2017, and local arrangement chair of ACM UMAP 2018. She has also been actively participating in industrial projects related to the research. Currently, she is a data scientist in Shopee Singapore with recommendation group.

Yin-Leng Theng is Professor and Director of the Centre of Healthy and Sustainable Cities (CHESS) at Wee Kim Wee School of Communication and Information, and Research Director at the Research Strategy and Coordination Unit (President’s Office) at Nanyang Technological University. Her research interests are mainly in user-centred design, interaction design and usability engineering. She has participated in varying capacities as principal investigator, co-investigator and collaborator in numerous research projects in the United Kingdom and Singapore since 1998.

Brief description : Recommender systems are indispensable tools to help tackle with the information overload problem. However, with merely relying on user-item interaction data, traditional recommender systems inherently suffer from the data sparsity and cold start issues. To address such issues, a number of recommendation algorithms have been designed by leveraging the valuable side information of users, items and their interactions to compensate for the insufficiency of rating information.

In this tutorial, we would provide a comprehensive analysis of state-of-the-art recommendation approaches with side information in a principle way from two perspectives: representation and methodology. By the end of this tutorial, the audiences would know how the recommendation approaches evolve with more complicated representations and methodologies for using various kinds of side information.