[38] Feng Wang and Huaping Liu. Understanding the behaviour of contrastive loss. In CVPR, 2021.
[39] Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and J. C. Mao. Deep crossing: Web-scale modeling without manually crafted combinatorial features. In KDD, 2016.
[40] Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. A generic coordinate descent framework for learning from implicit feedback. In WWW, 2017.
[41] Steffen Rendle. Item recommendation from implicit feedback. In Recommender Systems Handbook, pages 143-171. Springer US, 2022.
[42] Jason Weston, Samy Bengio, and Nicolas Usunier. WSABIE: scaling up to large vocabulary image annotation. In *IJCAI*, 2011.
[43] Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge J. Belongie, and Deborah Estrin. Collaborative metric learning. In WWW, 2017.
[44] Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A. Alemi, and George Tucker. On variational bounds of mutual information. In ICML, 2019.
[45] Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, and Yann LeCun. Decoupled contrastive learning. In ECCV, 2022.
[46] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. In NeurIPS, 2020.
[47] Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione, Marco Grangetto, and Pietro Gori. Unbiased supervised contrastive learning. In ICLR, 2023.
[48] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In ICLR, 2018.
[49] Zhaolin Hu and L Jeff Hong. Kullback-leibler divergence constrained distributionally robust optimization. Available at Optimization Online, 1(2):9, 2013.
[50] Yehuda Koren, Robert M. Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30-37, 2009.
[51] Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. Lightgen: Simplifying and powering graph convolution network for recommendation. In SIGIR, 2020.
[52] Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In WWW, 2022.
[53] Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, and Xiuqiang He. Simplex: A simple and strong baseline for collaborative filtering. In CIKM, 2021.
[54] An Zhang, Wenchang Ma, Xiang Wang, and Tat-Seng Chua. Incorporating bias-aware margins into contrastive loss for collaborative filtering. In NeurIPS, 2022.
[55] Jiawei Chen, Junkang Wu, Jiancan Wu, Xuezhi Cao, Sheng Zhou, and Xiangnan He. Adap-  $\tau$  : Adaptively modulating embedding magnitude for recommendation. In WWW, 2023.
[56] Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, and Tat-Seng Chua. Kuida: A fully-observed dataset and insights for evaluating recommender systems. In CIKM, 2022.
[57] Benjamin M. Marlin and Richard S. Zemel. Collaborative prediction and ranking with nonrandom missing data. In RecSys, 2009.
[58] Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. Recommendations as treatments: Debiasing learning and evaluation. In ICML, 2016.
[59] Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. Unbiased recommender learning from missing-not-at-random implicit feedback. In WSDM, 2020.