Junliang Yu

Email: junl.yu at outlook.com

Junliang Yu is currently a Lecturer (Assistant Professor) and ARC DECRA fellow with the School of Information and Communication Technology at Griffith University. Prior to this, he worked as a Research Fellow in the Data Science discipline with The University of Queensland (UQ) where he also completed his PhD in 2023 under the supervision of Prof. Hongzhi Yin. Before joining UQ, he obtained his M.Sc. and B.E. degrees from Chongqing University, where he was advised by Prof. Min Gao.

His research interests include recommender systems, data mining, generative AI, and agentic systems. He has published extensively in leading venues of these areas and has delivered four tutorials on recommender systems and graph machine learning at top-tier international conferences. His work has been cited for over 6,000 times. In addition, he is committed to open-source research and has developed two recommender system toolkits, QRec and SELFRec, which are actively used by researchers and practitioners.

News

Feb 5, 2026	I was invited to serve as Area Chair in KDD 2026.
Jan 14, 2026	We have one full paper on multi-agent systems and one demo paper on distribution drift accepted by WWW’26.
Nov 21, 2025	We have a paper on the robustness of contrastive recommender systems accepted by TOIS.
Nov 3, 2025	We have released the first survey on LLM-powered reasoning-aware recommender systems.
Oct 15, 2025	I am listed among the 2025 World’s Top 2% Scientists.
Apr 5, 2025	We have two papers on poisoning attacks against recommender systems accepted by SIGIR’25.
Jan 20, 2025	We have two papers on graph condensation and LLM accepted by WWW’25.
Dec 19, 2024	I have been promoted to a Level B position (Lecturer/Research Fellow).

Selected Publications

TKDE

XSimGCL: Towards extremely simple graph contrastive learning for recommendation

Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, and Hongzhi Yin

IEEE Transactions on Knowledge and Data Engineering, 2023
ESI Hot and Highly Cited Paper

PDF Code
SIGIR

Are graph augmentations necessary? simple graph contrastive learning for recommendation

Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022
The most cited paper in SIGIR’22, applied in Tencent Music

PDF Code
TKDE

Self-supervised learning for recommender systems: A survey

Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, and Zi Huang

IEEE Transactions on Knowledge and Data Engineering, 2022
ESI Hot and Highly Cited Paper

PDF Code
KDD

Socially-aware self-supervised tri-training for recommendation

Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
One of the most influential papers in KDD’21

PDF Code
WWW

Self-supervised multi-channel hypergraph convolutional network for social recommendation

Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang

Proceedings of the Web Conference, 2021
The 2nd most cited paper in WWW’21

PDF Code
TKDE

Enhancing social recommendation with adversarial graph convolutional networks

Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, and Lizhen Cui

IEEE Transactions on Knowledge and Data Engineering, 2020
ESI Highly Cited Paper

PDF Code