Dr Junliang Yu
ARC DECRA Research Fellow
School of Electrical Engineering and Computer Science
Researcher biography
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.
Journal Articles
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
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
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
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
Wang, Shiqi, Gao, Chongming, Gao, Min, Yu, Junliang, Wang, Zongwei and Yin, Hongzhi (2023). Who are the best adopters? User selection model for free trial item promotion. IEEE Transactions on Big Data, 9 (2), 746-757. doi: 10.1109/tbdata.2022.3205334
Wu, Fan, Gao, Min, Yu, Junliang, Wang, Zongwei, Liu, Kecheng and Wang, Xu (2021). Ready for emerging threats to recommender systems? A graph convolution-based generative shilling attack. Information Sciences, 578, 683-701. doi: 10.1016/j.ins.2021.07.041
Zhang, Junwei, Gao, Min, Yu, Junliang, Yang, Linda, Wang, Zongwei and Xiong, Qingyu (2021). Path-based reasoning over heterogeneous networks for recommendation via bidirectional modeling. Neurocomputing, 461, 438-449. doi: 10.1016/j.neucom.2021.07.038
Wang, Qinyong, Yin, Hongzhi, Chen, Tong, Yu, Junliang, Zhou, Alexander and Zhang, Xiangliang (2021). Fast-adapting and privacy-preserving federated recommender system. The VLDB Journal, 31 (5), 877-896. doi: 10.1007/s00778-021-00700-6
Gao, Min, Zhang, Junwei, Yu, Junliang, Li, Jundong, Wen, Junhao and Xiong, Qingyu (2021). Recommender systems based on generative adversarial networks: A problem-driven perspective. Information Sciences, 546, 1166-1185. doi: 10.1016/j.ins.2020.09.013
Yu, Junliang, Yin, Hongzhi, Li, Jundong, Gao, Min, Huang, Zi and Cui, Lizhen (2020). Enhance social recommendation with adversarial graph convolutional networks. IEEE Transactions on Knowledge and Data Engineering, 34 (8), 1-1. doi: 10.1109/tkde.2020.3033673
Yu, Junliang, Gao, Min, Rong, Wenge, Song, Yuqi and Xiong, Qingyu (2017). A social recommender based on factorization and distance metric learning. IEEE Access, 5 8066292, 21557-21566. doi: 10.1109/access.2017.2762459
Yu, Junliang, Gao, Min, Rong, Wenge, Li, Wentao, Xiong, Qingyu and Wen, Junhao (2017). Hybrid attacks on model-based social recommender systems. Physica A: Statistical Mechanics and its Applications, 483, 171-181. doi: 10.1016/j.physa.2017.04.048
Conference Papers
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
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
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
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
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
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
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
Wang, Zongwei, Gao, Min, Li, Wentao, Yu, Junliang, Guo, Linxin and Yin, Hongzhi (2023). Efficient bi-level optimization for recommendation denoising. 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, United States, 6-10 August 2023. New York, NY, United States: ACM. doi: 10.1145/3580305.3599324
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
Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Xu, Guandong and Nguyen, Quoc Viet Hung (2022). On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation. 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: 10.1145/3477495.3531775
Tommasini, Riccardo, Roy, Senjuti Basu, Wang, Xuan, Wang, Hongwei, Ji, Heng, Han, Jiawei, Nakov, Preslav, Da San Martino, Giovanni, Alam, Firoj, Schedl, Markus, Lex, Elisabeth, Bharadwaj, Akash, Cormode, Graham, Dojchinovski, Milan, Forberg, Jan, Frey, Johannes, Bonte, Pieter, Balduini, Marco, Belcao, Matteo, Della Valle, Emanuele, Yu, Junliang, Yin, Hongzhi, Chen, Tong, Liu, Haochen, Wang, Yiqi, Fan, Wenqi, Liu, Xiaorui, Dacon, Jamell, Lye, Lingjuan ... He, Xiangnan (2022). Accepted Tutorials at The Web Conference 2022. The Web Conference 2022, Lyon, France, 25 – 29 April 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3487553.3547182
Cui, Lizhen, Shao, Yingxia, Yu, Junliang, Yin, Hongzhi and Xia, Xin (2021). Self-supervised graph co-training for session-based recommendation. CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual, 1-5 November 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3459637.3482388
Zhang, Junwei, Gao, Min, Yu, Junliang, Guo, Lei, Li, Jundong and Yin, Hongzhi (2021). Double-scale self-supervised hypergraph learning for group recommendation. CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual, 1-5 November 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3459637.3482426
Yu, Junliang, Yin, Hongzhi, Gao, Min, Xia, Xin, Zhang, Xiangliang and Viet Hung, Nguyen Quoc (2021). Socially-aware self-supervised tri-training for recommendation. 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Virtual (Singapore), 14-18 August 2021. New York, NY, United States: ACM. doi: 10.1145/3447548.3467340
Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Cui, Lizhen and Zhang, Xiangliang (2021). Self-supervised hypergraph convolutional networks for session-based recommendation. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Virtual, 2-9 February 2021. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence.
Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Cui, Lizhen and Zhang, Xiangliang (2021). Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Online, 2–9 February 2021. Washington, DC United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v35i5.16578
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
Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Cui, Lizhen and Zhang, Xiangliang (2021). Self-supervised hypergraph convolutional networks for session-based recommendation. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, Virtual, 2-9 February 2021. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence Press.
Zhang, Junwei, Gao, Min, Yu, Junliang, Wang, Xinyi, Song, Yuqi and Xiong, Qingyu (2019). Nonlinear Transformation for Multiple Auxiliary Information in Music Recommendation. 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14-19 July 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IJCNN.2019.8851992
Wang, Zongwei, Gao, Min, Wang, Xinyi, Yu, Junliang, Wen, Junhao and Xiong, Qingyu (2019). A minimax game for generative and discriminative sample models for recommendation. 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Macau, China, 14-17 April 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-16145-3_33
Yu, Junliang, Gao, Min, Yin, Hongzhi, Li, Jundong, Gao, Chongming and Wang, Qinyong (2019). Generating reliable friends via adversarial training to improve social recommendation. IEEE International Conference on Data Mining , Beijing, China, 8-11 November 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDM.2019.00087
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
Song, Yuqi, Gao, Min, Yu, Junliang, Li, Wentao, Yu, Lulan and Xiao, Xinyu (2018). PUED: A Social Spammer Detection Method Based on PU Learning and Ensemble Learning. Springer Verlag. doi: 10.1007/978-3-030-00916-8_14
Fang, Qianqi, Liu, Ling, Yu, Junliang and Wen, Junhao (2018). Meta-path based heterogeneous graph embedding for music recommendation. Springer Verlag. doi: 10.1007/978-3-030-04182-3_10
Yu, Junliang, Gao, Min, Song, Yuqi, Fang, Qianqi, Rong, Wenge and Xiong, Qingyu (2018). Integrating User Embedding and Collaborative Filtering for Social Recommendations. Springer Verlag. doi: 10.1007/978-3-030-00916-8_44
Zhao, Zehua, Gao, Min, Yu, Junliang, Song, Yuqi, Wang, Xinyi and Zhang, Min (2018). Impact of the Important Users on Social Recommendation System. Springer Verlag. doi: 10.1007/978-3-030-00916-8_40
Dou, Tong, Yu, Junliang, Xiong, Qingyu, Gao, Min, Song, Yuqi and Fang, Qianqi (2018). Collaborative shilling detection bridging factorization and user embedding. 13th European Alliance for Innovation (EAI) International Conference on Collaborative Computing - Networking, Applications and Worksharing (CollaborateCom), Edinburgh, Scotland, 11-13 December 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-00916-8_43
Yang, Fan, Gao, Min, Yu, Junliang, Song, Yuqi and Wang, Xinyi (2018). Detection of shilling attack based on bayesian model and user embedding. 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, 5-7 November 2018. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ictai.2018.00102
Yu, Junliang, Gao, Min, Song, Yuqi, Zhao, Zehua, Rong, Wenge and Xiong, Qingyu (2017). Connecting factorization and distance metric learning for social recommendations. 10th International Conference on Knowledge Science, Engineering and Management (KSEM), Melbourne, VIC, Australia, 19-20 August 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-63558-3_33
Yu, Junliang, Gao, Min, Rong, Wenge, Song, Yuqi, Fang, Qianqi and Xiong, Qingyu (2017). Make users and preferred items closer: recommendation via distance metric learning. 24th International Conference on Neural Information Processing (ICONIP), Guangzhou, China, 14-18 November 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-70139-4_30
Song, Yuqi, Gao, Min, Yu, Junliang, Li, Wentao, Wen, Junhao and Xiong, Qingyu (2017). PUD: social spammer detection based on PU learning. 24th International Conference on Neural Information Processing (ICONIP), Guangzhou, China, 14-18 November 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-70139-4_18
Thesis
Yu, Junliang (2023). Enhancing recommender systems with self-supervised learning. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland. doi: 10.14264/9b4b38b