and Development in Information Retrieval, SIGIR '23, page 1498-1507, New York, NY, USA, 2023. Association for Computing Machinery.
[24] Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. Self-supervised graph learning for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '21, page 726-735, New York, NY, USA, 2021. Association for Computing Machinery.
[25] Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Ao Xiang, Xu Zhang, Leyu Lin, and Qing He. Selective fairness in recommendation via prompts. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22, page 2657-2662, New York, NY, USA, 2022. Association for Computing Machinery.
[26] Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xu Zhang, Leyu Lin, and Qing He. Personalized prompt for sequential recommendation, 2023.
[27] Haoran Yang, Hongxu Chen, Lin Li, Philip S. Yu, and Guandong Xu. Hyper meta-path contrastive learning for multi-behavior recommendation. In 2021 IEEE International Conference on Data Mining (ICDM), pages 787-796, 2021.
[28] Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S. Yu, and Guandong Xu. Dual space graph contrastive learning. In Proceedings of the ACM Web Conference 2022, WWW '22, page 1238-1247, New York, NY, USA, 2022. Association for Computing Machinery.
[29] Haoran Yang, Xiangyu Zhao, Muyang Li, Hongxu Chen, and Guandong Xu. Mitigating the performance sacrifice in dp-satisfied federated settings through graph contrastive learning. Information Sciences, 648:119552, 2023.
[30] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 5812-5823. Curran Associates, Inc., 2020.
[31] Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xi-angliang Zhang. Self-supervised multi-channel hypergraph convolutional network for social recommendation. In Proceedings of the Web Conference 2021, WWW '21, page 413-424, New York, NY, USA, 2021. Association for Computing Machinery.
[32] Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. Are graph augmentations necessary? simple graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22, page 1294–1303, New York, NY, USA, 2022. Association for Computing Machinery.
[33] Chi Zhang, Rui Chen, Xiangyu Zhao, Qilong Han, and Li Li. Denoising and prompt-tuning for multi-behavior recommendation. In Proceedings of the ACM Web Conference 2023, WWW '23, page 1355-1363, New York, NY, USA, 2023. Association for Computing Machinery.
[34] Guoshuai Zhao, Xueming Qian, and Xing Xie. User-service rating prediction by exploring social users' rating behaviors. IEEE Transactions on Multimedia, 18(3):496-506, 2016.
[35] Wayne Xin Zhao, Junhua Chen, Pengfei Wang, Qi Gu, and Ji-Rong Wen. Revisiting alternative experimental settings for evaluating top-n item recommendation algorithms. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM '20, page 2329-2332, New York, NY, USA, 2020. Association for Computing Machinery.
[36] Xin Zhou, Aixin Sun, Yong Liu, Jie Zhang, and Chunyan Miao. Selfcf: A simple framework for self-supervised collaborative filtering. ACM Trans. Recomm. Syst., 1(2), jun 2023.
[37] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference 2021, WWW '21, page 2069-2080, New York, NY, USA, 2021. Association for Computing Machinery.