[20] Jiaxi Tang and Ke Wang. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM, pages 565-573, 2018.
[21] Trinh Xuan Tuan and Minh Phuong Tu. 3d convolutional networks for session-based recommendation with content features. In RecSys, pages 138-146, 2017.
[22] Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. A simple convolutional generative network for next item recommendation. In WSDM, pages 582-590, 2019.
[23] Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, and Liang Wang. Dynamic graph neural networks for sequential recommendation. IEEE TKDE, 35(5):4741-4753, 2023.
[24] Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. Sequential recommendation with graph neural networks. In SIGIR, pages 378-387, 2021.
[25] Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. Graph contextualized self-attention network for session-based recommendation. In IJCAI, pages 3940–3946, 2019.
[26] Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. Session-based recommendation with graph neural networks. In AAAI, pages 346-353, 2019.
[27] Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, and Mark Coates. Memory augmented graph neural networks for sequential recommendation. In AAAI, pages 5045-5052, 2020.
[28] Wang-Cheng Kang and Julian McAuley. Self-attentive sequential recommendation. In ICDM, pages 197–206, 2018.
[29] 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 CIKM, pages 1441-1450, 2019.
[30] Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, and Robin Burke. Research commentary on recommendations with side information: a survey and research directions. Electronic Commerce Research and Applications (ECRA), 37, 2019.
[31] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE TNNLS, 2022.
[32] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In NeurIPS, pages 8536-8546, 2018.
[33] Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, and Masashi Sugiyama. How does disagreement help generalization against label corruption? In ICML, pages 7164-7173, 2019.
[34] Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An. Combating noisy labels by agreement: A joint training method with co-regularization. In CVPR, pages 13723–13732, 2020.
[35] Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, and Masashi Sugiyama. Sample selection with uncertainty of losses for learning with noisy labels. In ICLR, 2022.
[36] Yangdi Lu, Yang Bo, and Wenbo He. Noise attention learning: Enhancing noise robustness by gradient scaling. In NeurIPS, pages 23164-23177, 2022.
[37] Yangdi Lu and Wenbo He. SELC: self-ensemble label correction improves learning with noisy labels. In IJCAI, pages 3278–3284, 2022.
[38] Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, and Chao Chen. Error-bounded correction of noisy labels. In ICML, pages 11447-11457, 2020.