Junliang YU

prof_pic.jpg

Email: jl.yu at uq.edu.au

Hello, this is Junliang Yu. I am currently an ARC DECRA Research Fellow in the Data Science discipline at The University of Queensland (UQ). Prior to this, I worked as a postdoctoral research fellow with Prof. Shazia Sadiq, and completed my PhD at UQ in 2023 under the supervision of Prof. Hongzhi Yin. Before joining UQ, I obtained my M.Sc. and B.E. degrees from Chongqing University, where I was advised by Prof. Min Gao.

My research interests include recommender systems, graph learning, social network analysis, generative AI and multi-agent systems. As of September 2025, my publications have been cited over 5,000 times. Several of my works have been recognised as The Most Influential Papers by Paper Digest, and three journal articles have been listed as ESI Hot / Highly Cited Papers. I have also delivered four tutorials about recommender systems and graph machine learning at top-tier conferences, contributing to community knowledge-sharing and collaboration. In addition, I am committed to open-source development, and have created two recommender system toolkits, QRec and SELFRec, which are actively used by researchers and practitioners.


News

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).
Dec 6, 2024 We have a tutorial on graph condensation has been accepted for presentation at WWW’25.
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.

Selected Publications

  1. 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

    PDF Code
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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