[17] Ruining He and Julian McAuley. Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation. In IEEE 16th International Conference on Data Mining, pages 191-200, 2016.
[18] Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. Towards Universal Sequence Representation Learning for Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 585-593, 2022.
[19] Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus. Hard Negative Mixing for Contrastive Learning. In Advances in Neural Information Processing Systems, volume 33, pages 21798-21809, 2020.
[20] Wang-Cheng Kang and Julian McAuley. Self-Attentive Sequential Recommendation. In IEEE 18th International Conference on Data Mining, pages 197–206, 2018.
[21] Walid Krichene and Steffen Rendle. On Sampled Metrics for Item Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1748-1757, 2020.
[22] Shangsong Liang. Collaborative, Dynamic and Diversified User Profiling. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):4269-4276, 2019.
[23] Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, and Tie-Yan Liu. R-Drop: Regularized Dropout for Neural Networks. In Advances in Neural Information Processing Systems, volume 34, pages 10890–10905, 2021.
[24] Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, and Weipeng Yan. Kalman Filtering Attention for User Behavior Modeling in CTR Prediction. In Advances in Neural Information Processing Systems, volume 33, pages 9228-9238, 2020.
[25] Zheng Liu, Yu Xing, Fangzhao Wu, Mingxiao An, and Xing Xie. Hi-Fi Ark: Deep User Representation via High-Fidelity Archive Network. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 3059-3065, 2019.
[26] Zheng Liu, Jianxun Lian, Junhan Yang, Defu Lian, and Xing Xie. Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 289–298, 2020.
[27] Zhiwei Liu, Yongjun Chen, Jia Li, Philip S. Yu, Julian McAuley, and Caiming Xiong. Contrastive Self-supervised Sequential Recommendation with Robust Augmentation. arXiv preprint arXiv: 2108.06479, 2021.
[28] Lajanugen Logeswaran and Honglak Lee. An Efficient Framework for Learning Sentence Representations. In International Conference on Learning Representations, 2018.
[29] Ishan Misra and Laurens van der Maaten. Self-Supervised Learning of Pretext-Invariant Representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6707-6717, 2020.
[30] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation Learning with Contrastive Predictive Coding. arXiv preprint arXiv: 1807.03748, 2018.
[31] Emanuel Parzen. On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics, 33(3):1065-1076, 1962.
[32] Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining, pages 813-823, 2022.
[33] Zhaopeng Qiu, Xian Wu, Jingyue Gao, and Wei Fan. U-BERT: Pre-training User Representations for Improved Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5):4320-4327, 2021.