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2022 Conference Publication Are Graph Augmentations Necessary? : Simple Graph Contrastive Learning for RecommendationYu, 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 |
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2021 Conference Publication Self-supervised multi-channel hypergraph convolutional network for social recommendationYu, 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 |
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2018 Conference Publication Adaptive implicit friends identification over heterogeneous network for social recommendationYu, 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 |
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2024 Conference Publication Consistency and discrepancy-based contrastive tripartite graph learning for recommendationsGuo, 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 |
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2024 Conference Publication Unveiling vulnerabilities of contrastive recommender systems to poisoning attacksWang, 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 |
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2024 Conference Publication Accelerating scalable graph neural network inference with node-adaptive propagationGao, 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 |
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2024 Conference Publication Prompt-enhanced federated content representation learning for cross-domain recommendationGuo, 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 |
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2024 Conference Publication Motif-based prompt learning for universal cross-domain recommendationHao, 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 |
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2024 Journal Article XSimGCL: towards extremely simple graph contrastive learning for recommendationYu, 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 |
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2024 Journal Article Self-supervised learning for recommender systems: a surveyYu, 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 |
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2023 Conference Publication Towards communication-efficient model updating for on-device session-based recommendationXia, 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 |
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2023 Conference Publication Semantic-aware node synthesis for imbalanced heterogeneous information networksGao, 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 |
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2023 Journal Article Predictive and contrastive: dual-auxiliary learning for recommendationTao, 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 |
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2023 Journal Article Efficient on-device session-based recommendationXia, 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 |
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2023 Conference Publication Efficient bi-level optimization for recommendation denoisingWang, 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 |
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2023 Other Outputs Enhancing recommender systems with self-supervised learningYu, 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 |
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2023 Journal Article Who are the best adopters? User selection model for free trial item promotionWang, 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 |
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2023 Journal Article Bi-Graph Mix-random Walk Based Social Recommendation Model 基于双图混合随机游走的社会化推荐模型Cao, Yang, Gao, Min, Yu, Jun-Liang, Fan, Qi-Lin, Rong, Wen-Ge and Wen, Jun-Hao (2023). Bi-Graph Mix-random Walk Based Social Recommendation Model 基于双图混合随机游走的社会化推荐模型. Tien Tzu Hsueh Pao/Acta Electronica Sinica, 51 (2), 286-296. doi: 10.12263/DZXB.20210504 |
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2022 Conference Publication On-Device Next-Item Recommendation with Self-Supervised Knowledge DistillationXia, 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 |
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2022 Conference Publication Accepted Tutorials at The Web Conference 2022Tommasini, 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 |
