Junliang Yu

Email: jl.yu at uq.edu.au

Hello, this is Junliang Yu. I am currently an ARC DECRA Research Fellow with the Data Science discipline at The University of Queensland (UQ). Previously, I worked as a postdoctoral research fellow with Prof. Shazia Sadiq. I completed my PhD degree at UQ in 2023 under the supervision of Prof. Hongzhi Yin. Before my time at UQ, I earned my M.Sc. and B.E. degrees at Chongqing University, where I was supervised by Prof. Min Gao. I am dedicated to conducting influential and reproducible research. My work has received over 3,600 citations as of December 2024, with five of my conference papers being recognized as The Most Influential Papers by Paper Digest and three of my journal papers being recognized as ESI Hot / Highly Cited Papers in my research areas. I am also actively involved in the open-source community and have developed two popular recommender system frameworks, QRec and SELFRec, which have together garnered over 2,000 stars.

Research Interests

Recommender systems
Data-centric AI
Graph Learning
Large language models

Two fully funded PhD scholarships are available—one for an international student and one for a domestic student, with flexible enrolment in 2025. The PhD candidates will be co-supervised by me and Prof. Hongzhi Yin (associate supervisor). If you are interested in recommender systems and data science, please feel free to drop me an email and apply.

News

Dec 19, 2024	I have been promoted to a Level B position (Lecturer/Research Fellow).
Dec 6, 2024	We have a tutorial on graph condensation has been accepted for presentation at WWW’25.
Nov 14, 2024	I am listed among the world’s top 2% scientist released by Stanford University.
Oct 4, 2024	We have released a survey preprint on point-of-interest recommender systems.
Sep 20, 2024	We have released a preprint on LLM-powered attack against ID-free recommender systems.
Aug 24, 2024	I have been awarded the ARC DECRA Fellowship.
May 24, 2024	We have released a preprint on graph condensation acceleration.
May 17, 2024	Two co-authored papers on poisoning attacks and contrastive learning are accepted by KDD.

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 Highly Cited Paper

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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

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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

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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

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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

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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

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