Tutorial
Tutorial
Complement, Composite and Context:
The 3C-Law to Build Multidomain Recommender Systems
Complement, Composite and Context:
The 3C-Law to Build Multidomain Recommender Systems
Embedded Files
The 3C-Law to Build Multidomain Recommender Systems
Speakers: Dr Liang Hu, Dr Shoujin Wang, Dr Qi Zhang, Dr Zhong Yuan Lai and Dora D. Liu
Organizers: Dr Liang Hu, Dr Shoujin Wang, Dr Qi Zhang, Prof. Longbing Cao and Dora D. Liu
Time: Dec 8th, 4 PM - 6.30 PM, New Zealand time
Contact Mail: Dr Liang Hu, Dr Shoujin Wang
Abstract
This tutorial presents state-of-the-art theories and approaches to building multidomain recommender systems (RSs), including the latest and most advanced theories, methods, models, data, and applications. First, we will present the background and foundations of RSs, followed by the illustration of 3C-law (Complement, Composite, and Context) to build various multidomain RSs: (1) recommendation with complementary knowledge, (2) recommendation with composite knowledge, and (3) recommendation with contextual knowledge, together with their RS prototypes. Finally, we will demonstrate several representative emerging real-world application cases of multidomain RSs in fashion industry, finance, healthcare, and multimedia.
Agenda
Overview of multidomain recommender systems (slides)
Multi-item domain recommender systems (slides)
Multi-data domain recommender systems (slides)
Multi-user domain recommender systems (slides)
Multi-spatial domain recommender systems (slides)
Multi-temporal domain recommender systems (slides)
Multi-goal domain recommender systems (slides)
Summary (slides)
Other Speakers
Other Speakers
Shoujin Wang has been working as a research fellow at RMIT University, Australia.
He obtained his PhD in Data Analytics from University of Technology Sydney in 2018.
His main research interests include data mining, machine learning and recommender systems.
Qi Zhang received his first Ph.D. from the Department of Computer Science and Engineering, Beijing Institute of Technology, China in 2020.
Currently, he is an AI scientist in DeepBlue Academy of Sciences, and a Ph.D. candidate in Analytics at University of Technology Sydney, Australia.
His research interests include recommender systems, learning to hash, machine learning and general artificial intelligence.
Dr Zhong Yuan Lai
Dr Zhong Yuan Lai
Dr. Zhong Yuan Lai obtained his PhD from the University of Bonn, Germany in 2017.
He was subsequently postdoc researcher at Fudan University before assuming his current position as researcher at the DeepBlue Academy of Sciences.
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