Overview
Background
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.
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
Works
Search Professor Junliang Yu’s works on UQ eSpace
2021
Journal Article
Ready for emerging threats to recommender systems? A graph convolution-based generative shilling attack
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
2021
Conference Publication
Self-supervised graph co-training for session-based recommendation
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
2021
Conference Publication
Double-scale self-supervised hypergraph learning for group recommendation
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
2021
Journal Article
Path-based reasoning over heterogeneous networks for recommendation via bidirectional modeling
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
2021
Journal Article
Fast-adapting and privacy-preserving federated recommender system
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
2021
Conference Publication
Socially-aware self-supervised tri-training for recommendation
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
2021
Conference Publication
Self-supervised hypergraph convolutional networks for session-based recommendation
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.
2021
Conference Publication
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
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
2021
Journal Article
Recommender systems based on generative adversarial networks: A problem-driven perspective
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
2021
Conference Publication
Self-supervised hypergraph convolutional networks for session-based recommendation
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.
2020
Journal Article
Enhance social recommendation with adversarial graph convolutional networks
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
2019
Conference Publication
Nonlinear Transformation for Multiple Auxiliary Information in Music Recommendation
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
2019
Conference Publication
Generating reliable friends via adversarial training to improve social recommendation
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
2019
Conference Publication
A minimax game for generative and discriminative sample models for recommendation
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
2018
Conference Publication
Collaborative shilling detection bridging factorization and user embedding
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
2018
Conference Publication
Meta-path based heterogeneous graph embedding for music recommendation
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
2018
Conference Publication
Integrating User Embedding and Collaborative Filtering for Social Recommendations
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
2018
Conference Publication
Impact of the Important Users on Social Recommendation System
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
2018
Conference Publication
Detection of shilling attack based on bayesian model and user embedding
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
2018
Conference Publication
PUED: A Social Spammer Detection Method Based on PU Learning and Ensemble Learning
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
Supervision
Availability
- Dr Junliang Yu is:
- Available for supervision
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Media
Enquiries
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