Brief Biography

Shuai Zhang is a postdoctoral researcher in the department of computer science at ETH Zurich, where he works with Prof. Ce Zhang. He received his PhD in computer science from the University of New South Wales, under the supervision of Prof. Lina Yao. He is a recipient of the outstanding paper award at ICLR 2021 and the best paper award runner-up at WSDM 2020. If you’d like to know more about my work or explore opportunities for collaboration, please get in touch!

Latest News

  • 18-05-2021: Two papers accepted to KDD 2021.
  • 06-05-2021: one short paper accepted to ACL 2021.
  • 30-04-2021: give a talk in the CVG group of Universität Bern.
  • 01-04-2021: Received the ICLR 2021 Outstanding Paper Award.

Selected Publications

Deep Learning for Recommender Systems, book chapter, to appear
Shuai Zhang, Yi Tay, Lina Yao, Aixin Sun, Ce Zhang.
Book Title: the 3rd edition of the recommender systems handbook.
Book Editors: Francesco Ricci, Lior Rokach, Bracha Shapira.
Publisher: Springer. 2021

Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n Parameters
Aston Zhang, Yi Tay, Shuai Zhang, Alvin Chan, Anh Tuan Luu, Siu Hui, Jie Fu.
The International Conference on Learning Representations, 2021
Outstanding Paper Award.

Learning User Representations with Hypercuboids for Recommender Systems
Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He and Wenwu Ou.
The 14th ACM International Conference on Web Search and Data Mining, 2021

HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiaoli Li.
The 13th ACM International Conference on Web Search and Data Mining, 2020
Best Paper Award Runner-up.

Quaternion Knowledge Graph Embeddings
Shuai Zhang, Yi Tay, Lina Yao, Qi Liu.
Thirty-third Conference on Neural Information Processing Systems, 2019.


Wanna learn more about deep learning and recommender systems? You can: (1) Check our survey on deep learning based recommender systems; (2) Read our book chapter Recommender Systems in Dive into Deep Learning; (3) Get your hands dirty with our opensource toolkit: DeepRec.

“Tell me and I forget, teach me and I may remember, involve me and I learn.” - Benjamin Franklin