Overview
Background
Junliang Yu is currently an ARC DECRA Fellow with the Data Science discipline at The University of Queensland (UQ). Previously, he worked as a postdoctoral research fellow with Prof. Shazia Sadiq. He completed his PhD degree at UQ in 2023 under the supervision of Prof. Hongzhi Yin. Before his time at UQ, he earned his M.Sc. and B.E. degrees at Chongqing University, where he was supervised by Prof. Min Gao.
Availability
- Dr Junliang Yu is:
- Available for supervision
Qualifications
- Bachelor of Software Engineering, Chongqing University
- Masters (Research) of Software Engineering, Chongqing University
- Doctor of Philosophy of Data Science, The University of Queensland
Research interests
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Self-Supervised Learning
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Recommender Systems
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Tiny Machine Learning
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Data-Centric AI
Research impacts
He is dedicated to conducting influential and reproducible research. His work has received over 3,600 citations as of December 2024, with five of his 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 his research areas. He is 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.
Works
Search Professor Junliang Yu’s works on UQ eSpace
Featured
2022
Conference Publication
Are Graph Augmentations Necessary? : Simple Graph Contrastive Learning for Recommendation
Yu, Junliang, Yin, Hongzhi, Xia, Xin, Chen, Tong, Cui, Lizhen and Nguyen, Quoc Viet Hung (2022). Are Graph Augmentations Necessary? : Simple Graph Contrastive Learning for Recommendation. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3531937
Featured
2021
Conference Publication
Self-supervised multi-channel hypergraph convolutional network for social recommendation
Yu, Junliang, Yin, Hongzhi, Li, Jundong, Wang, Qinyong, Hung, Nguyen Quoc Viet and Zhang, Xiangliang (2021). Self-supervised multi-channel hypergraph convolutional network for social recommendation. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-23 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449844
Featured
2018
Conference Publication
Adaptive implicit friends identification over heterogeneous network for social recommendation
Yu, Junliang, Gao, Min, Li, Jundong, Yin, Hongzhi and Liu, Huan (2018). Adaptive implicit friends identification over heterogeneous network for social recommendation. 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22-26 October 2018. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.1145/3269206.3271725
2025
Conference Publication
BiasNavi: LLM-Empowered Data Bias Management
Yu, Junliang, Huynh, Jay Thai Duong, Fan, Shaoyang, Demartini, Gianluca, Chen, Tong, Yin, Hongzhi and Sadiq, Shazia (2025). BiasNavi: LLM-Empowered Data Bias Management. New York, NY, USA: ACM. doi: 10.1145/3701716.3715169
2025
Conference Publication
Graph Condensation: Foundations, Methods and Prospects
Yin, Hongzhi, Gao, Xinyi, Yu, Junliang, Qiu, Ruihong, Chen, Tong, Nguyen, Quoc Viet Hung and Huang, Zi (2025). Graph Condensation: Foundations, Methods and Prospects. New York, NY, USA: ACM. doi: 10.1145/3701716.3715862
2025
Conference Publication
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
Gao, Xinyi, Ye, Guanhua, Chen, Tong, Zhang, Wentao, Yu, Junliang and Yin, Hongzhi (2025). Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition. WWW '25: The ACM Web Conference 2025, Sydney, NSW Australia, 28 April - 2 May 2025. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3696410.3714916
2025
Journal Article
Graph condensation: a survey
Gao, Xinyi, Yu, Junliang, Chen, Tong, Ye, Guanhua, Zhang, Wentao and Yin, Hongzhi (2025). Graph condensation: a survey. IEEE Transactions on Knowledge and Data Engineering, 37 (4), 1819-1837. doi: 10.1109/tkde.2025.3535877
2025
Conference Publication
Towards Secure and Robust Recommender Systems: A Data-Centric Perspective
Wang, Zongwei, Yu, Junliang, Chen, Tong, Yin, Hongzhi, Sadiq, Shazia and Gao, Min (2025). Towards Secure and Robust Recommender Systems: A Data-Centric Perspective. 18th International Conference on Web Search and Data Mining-WSDM, Hannover Germany, Mar 10-14, 2025. New York, NY, USA: ACM. doi: 10.1145/3701551.3703484
2025
Journal Article
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
Zhang, Qianru, Yang, Peng, Yu, Junliang, Wang, Haixin, He, Xingwei, Yiu, Siu-Ming and Yin, Hongzhi (2025). A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-20. doi: 10.1109/tkde.2025.3551292
2024
Conference Publication
Unveiling vulnerabilities of contrastive recommender systems to poisoning attacks
Wang, Zongwei, Yu, Junliang, Gao, Min, Yin, Hongzhi, Cui, Bin and Sadiq, Shazia (2024). Unveiling vulnerabilities of contrastive recommender systems to poisoning attacks. 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671795
2024
Conference Publication
Consistency and discrepancy-based contrastive tripartite graph learning for recommendations
Guo, Linxin, Zhu, Yaochen, Gao, Min, Tao, Yinghui, Yu, Junliang and Chen, Chen (2024). Consistency and discrepancy-based contrastive tripartite graph learning for recommendations. 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3672056
2024
Conference Publication
Prompt-enhanced federated content representation learning for cross-domain recommendation
Guo, Lei, Lu, Ziang, Yu, Junliang, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2024). Prompt-enhanced federated content representation learning for cross-domain recommendation. 33rd ACM Web Conference, WWW 2024, Singapore, 13 May 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3589334.3645337
2024
Conference Publication
Accelerating scalable graph neural network inference with node-adaptive propagation
Gao, Xinyi, Zhang, Wentao, Yu, Junliang, Shao, Yingxia, Nguyen, Quoc Viet Hung, Cui, Bin and Yin, Hongzhi (2024). Accelerating scalable graph neural network inference with node-adaptive propagation. 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 13-16 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde60146.2024.00236
2024
Conference Publication
Motif-based prompt learning for universal cross-domain recommendation
Hao, Bowen, Yang, Chaoqun, Guo, Lei, Yu, Junliang and Yin, Hongzhi (2024). Motif-based prompt learning for universal cross-domain recommendation. 17th ACM International Conference on Web Search and Data Mining (WSDM), Merida, Mexico, 4-8 March 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3616855.3635754
2024
Journal Article
XSimGCL: towards extremely simple graph contrastive learning for recommendation
Yu, Junliang, Xia, Xin, Chen, Tong, Cui, Lizhen, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2024). XSimGCL: towards extremely simple graph contrastive learning for recommendation. IEEE Transactions on Knowledge and Data Engineering, 36 (2), 913-926. doi: 10.1109/tkde.2023.3288135
2024
Journal Article
Self-supervised learning for recommender systems: a survey
Yu, Junliang, Yin, Hongzhi, Xia, Xin, Chen, Tong, Li, Jundong and Huang, Zi (2024). Self-supervised learning for recommender systems: a survey. IEEE Transactions on Knowledge and Data Engineering, 36 (1), 335-355. doi: 10.1109/tkde.2023.3282907
2023
Conference Publication
Towards communication-efficient model updating for on-device session-based recommendation
Xia, Xin, Yu, Junliang, Xu, Guandong and Yin, Hongzhi (2023). Towards communication-efficient model updating for on-device session-based recommendation. 32nd ACM International Conference on Information and Knowledge Management (CIKM), Birmingham, United Kingdom, 21-25 October 2023. New York, NY, United States: ACM. doi: 10.1145/3583780.3615088
2023
Conference Publication
Semantic-aware node synthesis for imbalanced heterogeneous information networks
Gao, Xinyi, Zhang, Wentao, Chen, Tong, Yu, Junliang, Nguyen, Hung Quoc Viet and Yin, Hongzhi (2023). Semantic-aware node synthesis for imbalanced heterogeneous information networks. 32nd ACM International Conference on Information and Knowledge Management, Birmingham, United Kingdom, 21–25 October 2023. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3583780.3615055
2023
Journal Article
Predictive and contrastive: dual-auxiliary learning for recommendation
Tao, Yinghui, Gao, Min, Yu, Junliang, Wang, Zongwei, Xiong, Qingyu and Wang, Xu (2023). Predictive and contrastive: dual-auxiliary learning for recommendation. IEEE Transactions on Computational Social Systems, 10 (5), 2254-2265. doi: 10.1109/TCSS.2022.3185714
2023
Journal Article
Efficient on-device session-based recommendation
Xia, Xin, Yu, Junliang, Wang, Qinyong, Yang, Chaoqun, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2023). Efficient on-device session-based recommendation. ACM Transactions on Information Systems, 41 (4) 102, 1-24. doi: 10.1145/3580364
Funding
Current funding
Supervision
Availability
- Dr Junliang Yu is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Supervision history
Current supervision
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Doctor Philosophy
Chain-of-User-Thought for Personalized Agent in Cyber World
Associate Advisor
Other advisors: Professor Hongzhi Yin
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Doctor Philosophy
Scalable and Lightweight On-Device Recommender Systems
Associate Advisor
Other advisors: Professor Hongzhi Yin, Dr Rocky Chen
Media
Enquiries
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