[34] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pages 452-461, 2009.
[35] Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. Contrastive Learning with Hard Negative Samples. In International Conference on Learning Representations, 2021.
[36] Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pages 1161–1170, 2019.
[37] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pages 1441-1450, 2019.
[38] Qinghui Sun, Jie Gu, XiaoXiao Xu, Renjun Xu, Ke Liu, Bei Yang, Hong Liu, and Huan Xu. Learning Interest-Oriented Universal User Representation via Self-Supervision. In Proceedings of the 30th ACM International Conference on Multimedia, pages 7270-7278, 2022.
[39] Jie Tang, Limin Yao, Duo Zhang, and Jing Zhang. A Combination Approach to Web User Profiling. ACM Transactions on Knowledge Discovery from Data, 5(1):1-44, 2010.
[40] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In Advances in Neural Information Processing Systems, volume 30, pages 5998-6008, 2017.
[41] Petar Velicković, William Fedus, William L. Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm. Deep Graph Infomax. In International Conference on Learning Representations, 2019.
[42] Feng Wang and Huaping Liu. Understanding the Behaviour of Contrastive Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2495-2504, 2021.
[43] Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, and Bo Han. Learning with Group Noise. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11):10192-10200, 2021.
[44] Weilun Wang, Wengang Zhou, Jianmin Bao, Dong Chen, and Houqiang Li. Instance-Wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14020–14029, 2021.
[45] Chuhan Wu, Fangzhao Wu, Tao Qi, Jianxun Lian, Yongfeng Huang, and Xing Xie. PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1939-1944, 2020.
[46] Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. UserBERT: Pre-Training User Model with Contrastive Self-Supervision. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2087–2092, 2022.
[47] Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. Contrastive Learning for Sequential Recommendation. In IEEE 38th International Conference on Data Engineering, pages 1259–1273, 2022.
[48] Runlong Yu, Yunzhou Zhang, Yuyang Ye, Le Wu, Chao Wang, Qi Liu, and Enhong Chen. Multiple Pairwise Ranking with Implicit Feedback. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 1727-1730, 2018.
[49] Wenhui Yu and Zheng Qin. Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 861-870, 2020.