ICEBE 2021 Keynotes
Keynote I - User-Item Matching and Auto-encoders for Cold-start Next-Item Recommendation

Dr. Michael Ng
Professor, Department of Mathematics, The University of Hong Kong


Recommendation systems provide personalized service to users and aim at suggesting to them items that they may prefer. There is an increasing requirement of next-item recommendation systems to infer a user’s next favor item based on his/her historical selection of items. In this talk, we study the next-item recommendation under the cold-start situation, where the users in the system share no interaction with the new items. We propose and study a novel model called User-Item Matching and Auto-encoders, which learns the latent embeddings for both users and items by exploiting user historical preferences and item attributes. The relevant hypergraph collaborative networks will also be discussed in the talk.


Michael Ng is a Chair Professor, Department of Mathematics, The University of Hong Kong. He is elected as a SIAM Fellow in 2020, and won the 12th Feng Kang Scientific Computing Prize in the same year to honor his outstanding contributions to solving structured linear systems in numerical linear algebra, mathematics and calculation methods in image processing. He is the world's Top 2% most-cited scientists, HKU Scholars in the Top 1%. His research interests including Applied and Computational Mathematics, Artificial Intelligence and Machine Learning, Data and Imaging Sciences and Scientific Computing.

Keynote II - Interactions of agents in virtual societies: how they learn, what we learn, and what is next?

Dr. Ho-fung Leung
Professor, Department of Computer Science and Engineering, CUHK
Professor (by courtesy), Department of Sociology, CUHK


Interactions in Multiagent Systems has been one of the active research areas in Artificial Intelligence. It studies ‘intelligent agents’ and their interactions in ‘multi-agent systems,’ such as the phenomenon of norm emergence as a result of multiagent learning in multiagent systems. In the process of interaction, agents learn how to better interact with other agents through their own experiences, which leads to changes in individual and global behaviours. In this talk I shall start by introducing the concept of intelligent agents, and how they learn and adapt in interactions. I shall also explore what we learn from these agent simulation experiments, using some research works done by us in the recent years for illustration. Finally, I shall introduce our recent work on mathematical modelling of the dynamics of the systems.


Professor Ho-fung Leung is a Professor in the Department of Computer Science and Engineering and a Professor (by courtesy) in the Department of Sociology at The Chinese University of Hong Kong. He is the Director of the MSc Programme in Computer Science. His research interests cover various aspects centring around artificial intelligence, including multiagent systems (reinforcement learning, emergence phenomena, and evolution dynamics), game theoretic analysis, ontologies (knowledge graphs), and big data analytics. Professor Leung has authored more than 250 publications, including 5 research monographs, and 5 edited volumes.

Professor Leung was the chairperson of ACM (Hong Kong Chapter) in 1998. He is a Chartered Fellow of the BCS, a Fellow of the HKIE, and a full member the HKCS. He is a Chartered Engineer registered by the Engineering Council.

Professor Leung received his BSc and MPhil degrees in Computer Science from The Chinese University of Hong Kong, and his PhD degree from University of London with DIC (Diploma of Imperial College) in Computing from Imperial College London.

© 2020-2021 IEEE International Conference on e-Business Engineering (ICEBE 2021). All rights reserved.