Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Speaker: Dr. Chen GAO
         Department of Electronic Engineering
         Tsinghua University

Title:   "Graph Neural Networks for Recommender Systems: Challenges,
         Methods, and Directions"

Date:    Monday, 8 November 2021

Time:    4:00pm - 5:00pm (Hong Kong Local Time)

Zoom link:
https://hkust.zoom.us/j/95532049042?pwd=UjkvVG9oZEhqZ1A5M2NJbWplelRJQT09

Meeting ID:     955 3204 9042
Passcode:       CSE

Abstract:

Recommender system is one of the most important information services on
today's Internet. Recently, graph neural networks have become the new
state-of-the-art approach of recommender systems. For recommender systems,
in general, there are four aspects for categorizing existing works: stage,
scenario, objective, and application. For graph neural networks, the
existing methods consist of two categories, spectral models and spatial
ones. We then discuss the motivation of applying graph neural networks
into recommender systems, mainly consisting of the high-order
connectivity, the structural property of data, and the enhanced
supervision signal. We then systematically analyze the challenges in graph
construction, embedding propagation/aggregation, model optimization, and
computation efficiency. Afterward and primarily, we discuss existing works
of graph neural network-based recommender systems, following the taxonomy
above. Finally, we raise discussions on the open problems and promising
future directions of this area.


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Biography:

Dr. Chen Gao is now a Postdoc Researcher at the Department of Electronic
Engineering, Tsinghua University. He obtained his Ph.D. Degree and
Bachelor Degree from the same department in 2021 and 2016, respectively.
His research mainly focuses on data mining and information retrieval,
especially on recommender system. He has over 30 publications in journals
and conferences such as SIGIR, WWW, ICLR, KDD, IJCAI, ICDE, TKDE, TKDD,
etc. His work on GNN-based bundle recommendation received the Best Short
Paper Honorable Mention Award in SIGIR 2020. He is also selected as Top
100 Chinese New Stars in Artificial Intelligence (Data Mining Area) by
Baidu Scholar.