Dr Junliang Yu

Postdoctoral Research Fellow

School of Electrical Engineering and Computer Science
Faculty of Engineering, Architecture and Information Technology

Overview

Junliang Yu is currently a Postdoctoral Research Fellow with with the Data Science Discipline, School of Electrical Engineering and Computer Science, The University of Queensland. Prior to that, he completed his PhD degree at UQ, Master and Bachelor degrees at Chongqing University. His research interests include data mining, recommender systems, and data-centric machine learing. He works with Prof. Shazia Sadiq and A/Prof. Hongzhi Yin.

Research Interests

  • Self-Supervised Learning
  • Recommender Systems
  • Tiny Machine Learning
  • Data-Centric AI

Qualifications

  • Doctor of Philosophy, The University of Queensland

Publications

  • 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

  • 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

  • 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

View all Publications

Publications

Featured Publications

  • 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

  • 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

  • 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

Journal Article

Conference Publication

  • 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

  • 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

  • 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

  • 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.

  • 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.

  • 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

  • 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

  • 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