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Liang QU

Bio

I am a third-year data science Ph.D. student jointly supervised by The University of Queensland (UQ) and Southern University of Science and Technology (SUSTech), advised by Prof. Yuhui Shi and Prof. Hongzhi Yin.

I received a B.E. in Applied Physics in 2017, and an M.S. in Computer Science in 2019, from South China University of Technology (SCUT) and Harbin Institute of Technology (HIT), respectively.

Research Interests

  • Recommender System
  • Graph Embedding
  • Federated Learning
  • Automatic Machine Learning

Selected Publications

Research Papers

  1. Liang Qu, Wei Yuan, Ruiqi Zheng, Lizhen Cui, Yuhui Shi, and Hongzhi Yin. “Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation.” In Proceedings of the ACM Web Conference 2024 (WWW ‘24) Paper (CCF A and CORE A*).
  2. Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, and Hongzhi Yin. 2023. Semi-decentralized Federated Ego Graph Learning for Recommendation. In Proceedings of the ACM Web Conference 2023 (WWW ‘23). Association for Computing Machinery, New York, NY, USA, 339–348. Paper (CCF A and CORE A*)
  3. Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi, and Hongzhi Yin. 2022. Single-shot Embedding Dimension Search in Recommender System. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22), July 11–15, 2022, Madrid, Spain. ACM, New York, NY, USA, 10 pages. Paper (CCF A and CORE A*)
  4. Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, and Hongzhi Yin. 2021. ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21), August 14–18, 2021, Virtual Event, Singapore. ACM, New York, NY, USA, 9 pages. Paper, Code (CCF A and CORE A*)
  5. Liang Qu, Huaisheng Zhu, Qiqi Duan, and Yuhui Shi. 2020. Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network. In Proceedings of TheWeb Conference 2020 (WWW’20), April 20–24, 2020, Taipei,Taiwan. ACM, New York, NY, USA, 7 pages. Paper, Code (CCF A and CORE A*)

Survey Papers

  1. Hongzhi Yin, Liang Qu, Tong Chen, Wei Yuan, Ruiqi Zheng, Jing Long, Xin Xia, Yuhui Shi, and Chengqi Zhang. “On-Device Recommender Systems: A Comprehensive Survey.” arXiv preprint arXiv:2401.11441 (2024). Paper (Co-first author)

  2. Ruiqi Zheng, Liang Qu, Bin Cui, Yuhui Shi, and Hongzhi Yin. 2023. AutoML for Deep Recommender Systems: A Survey. ACM Trans. Inf. Syst. 41, 4, Article 101 (October 2023), 38 pages. Paper (Co-first author)

Professinoal Services

  • Journal Reviewer: TKDE, TNNLS, TOIS, et al.
  • Conference PC Member (Reviewer): SIGKDD 2024, IJCAI 2024, WWW2024, CIKM 2023, VLDB 2022, SIGKDD 2022, WWW 2022, PAKDD 2022, DASFAA 2022, WISE 2021, VLDB 2021

Work Experiences