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In IJCAI. 1579-1585.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0127"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33374"},{"key":"e_1_3_2_1_62_1","first-page":"17","article-title":"Improved recurrent neural networks for session-based recommendations","author":"Tan Yong Kiam","year":"2016","unstructured":"Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In DLRS. 17-22.","journal-title":"DLRS."},{"key":"e_1_3_2_1_63_1","first-page":"565","article-title":"Personalized top-n sequential recommendation via convolutional sequence embedding","author":"Tang Jiaxi","year":"2018","unstructured":"Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM. 565-573.","journal-title":"WSDM."},{"key":"e_1_3_2_1_64_1","volume-title":"Attention is all you need. NIPS","author":"Vaswani A","year":"2017","unstructured":"A Vaswani. 2017. Attention is all you need. NIPS (2017)."},{"key":"e_1_3_2_1_65_1","volume-title":"EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis. arXiv preprint arXiv:2602.02551","author":"Wang Hua","year":"2026","unstructured":"Hua Wang, Jinghao Lu, and Fan Zhang. 2026. EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis. arXiv preprint arXiv:2602.02551 (2026)."},{"key":"e_1_3_2_1_66_1","first-page":"3562","article-title":"Causal representation learning for out-of-distribution recommendation","author":"Wang Wenjie","year":"2022","unstructured":"Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua. 2022. Causal representation learning for out-of-distribution recommendation. In WWW. 3562-3571.","journal-title":"WWW."},{"key":"e_1_3_2_1_67_1","first-page":"347","article-title":"Counterfactual data-augmented sequential recommendation","author":"Wang Zhenlei","year":"2021","unstructured":"Zhenlei Wang, Jingsen Zhang, Hongteng Xu, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao, and Ji-Rong Wen. 2021. Counterfactual data-augmented sequential recommendation. In SIGIR. 347-356.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_68_1","first-page":"9329","article-title":"Diff4rec: Sequential recommendation with curriculum-scheduled diffusion augmentation","author":"Wu Zihao","year":"2023","unstructured":"Zihao Wu, Xin Wang, Hong Chen, Kaidong Li, Yi Han, Lifeng Sun, and Wenwu Zhu. 2023. Diff4rec: Sequential recommendation with curriculum-scheduled diffusion augmentation. In MM. 9329-9335.","journal-title":"MM."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3701551.3703542"},{"key":"e_1_3_2_1_70_1","first-page":"6099","article-title":"Multi-behavior sequential recommendation with temporal graph transformer","volume":"35","author":"Xia Lianghao","year":"2022","unstructured":"Lianghao Xia, Chao Huang, Yong Xu, and Jian Pei. 2022. Multi-behavior sequential recommendation with temporal graph transformer. TKDE, Vol. 35, 6 (2022), 6099-6112.","journal-title":"TKDE"},{"key":"e_1_3_2_1_71_1","first-page":"1259","article-title":"Contrastive learning for sequential recommendation","author":"Xie Xu","year":"2022","unstructured":"Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In ICDE. IEEE, 1259-1273.","journal-title":"ICDE. 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In CIKM. 5381-5385.","journal-title":"CIKM."},{"key":"e_1_3_2_1_74_1","first-page":"3173","article-title":"Rethinking cross-domain sequential recommendation under open-world assumptions","author":"Xu Wujiang","year":"2024","unstructured":"Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, and Junchi Yan. 2024. Rethinking cross-domain sequential recommendation under open-world assumptions. In WWW. 3173-3184.","journal-title":"WWW."},{"key":"e_1_3_2_1_75_1","first-page":"331","article-title":"A generic learning framework for sequential recommendation with distribution shifts","author":"Yang Zhengyi","year":"2023","unstructured":"Zhengyi Yang, Xiangnan He, Jizhi Zhang, Jiancan Wu, Xin Xin, Jiawei Chen, and Xiang Wang. 2023. A generic learning framework for sequential recommendation with distribution shifts. In SIGIR. 331-340.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_76_1","first-page":"1459","article-title":"Time matters: Sequential recommendation with complex temporal information","author":"Ye Wenwen","year":"2020","unstructured":"Wenwen Ye, Shuaiqiang Wang, Xu Chen, Xuepeng Wang, Zheng Qin, and Dawei Yin. 2020. Time matters: Sequential recommendation with complex temporal information. In SIGIR. 1459-1468.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_77_1","first-page":"321","article-title":"Graph masked autoencoder for sequential recommendation","author":"Ye Yaowen","year":"2023","unstructured":"Yaowen Ye, Lianghao Xia, and Chao Huang. 2023. Graph masked autoencoder for sequential recommendation. In SIGIR. 321-330.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_78_1","first-page":"3447","article-title":"Overcoming data sparsity in group recommendation","volume":"34","author":"Yin Hongzhi","year":"2020","unstructured":"Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, and Xiaofang Zhou. 2020. Overcoming data sparsity in group recommendation. TKDE, Vol. 34, 7 (2020), 3447-3460.","journal-title":"TKDE"},{"key":"e_1_3_2_1_79_1","first-page":"3954","article-title":"Dataset regeneration for sequential recommendation","author":"Yin Mingjia","year":"2024","unstructured":"Mingjia Yin, Hao Wang, Wei Guo, Yong Liu, Suojuan Zhang, Sirui Zhao, Defu Lian, and Enhong Chen. 2024. Dataset regeneration for sequential recommendation. In KDD. 3954-3965.","journal-title":"KDD."},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290975"},{"key":"e_1_3_2_1_81_1","first-page":"930","article-title":"Linear recurrent units for sequential recommendation","author":"Yue Zhenrui","year":"2024","unstructured":"Zhenrui Yue, Yueqi Wang, Zhankui He, Huimin Zeng, Julian McAuley, and Dong Wang. 2024. Linear recurrent units for sequential recommendation. In WSDM. 930-938.","journal-title":"WSDM."},{"key":"e_1_3_2_1_82_1","first-page":"1","volume-title":"TOIS","volume":"41","author":"Zang Tianzi","year":"2022","unstructured":"Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, and Jiadi Yu. 2022. A survey on cross-domain recommendation: taxonomies, methods, and future directions. TOIS, Vol. 41, 2 (2022), 1-39."},{"key":"e_1_3_2_1_83_1","first-page":"5453","article-title":"Federated adaptation for foundation model-based recommendations","author":"Zhang Chunxu","year":"2024","unstructured":"Chunxu Zhang, Guodong Long, Hongkuan Guo, Xiao Fang, Yang Song, Zhaojie Liu, Guorui Zhou, Zijian Zhang, Yang Liu, and Bo Yang. 2024c. Federated adaptation for foundation model-based recommendations. In IJCAI. 5453-5461.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_84_1","volume-title":"Ninerec: A benchmark dataset suite for evaluating transferable recommendation. TPAMI","author":"Zhang Jiaqi","year":"2024","unstructured":"Jiaqi Zhang, Yu Cheng, Yongxin Ni, Yunzhu Pan, Zheng Yuan, Junchen Fu, Youhua Li, Jie Wang, and Fajie Yuan. 2024a. Ninerec: A benchmark dataset suite for evaluating transferable recommendation. TPAMI (2024)."},{"key":"e_1_3_2_1_85_1","first-page":"3434","article-title":"Adaptive disentangled transformer for sequential recommendation","author":"Zhang Yipeng","year":"2023","unstructured":"Yipeng Zhang, Xin Wang, Hong Chen, and Wenwu Zhu. 2023. Adaptive disentangled transformer for sequential recommendation. In KDD. 3434-3445.","journal-title":"KDD."},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33456"},{"key":"e_1_3_2_1_87_1","first-page":"893","article-title":"M3oe: Multi-domain multi-task mixture-of experts recommendation framework","author":"Zhang Zijian","year":"2024","unstructured":"Zijian Zhang, Shuchang Liu, Jiaao Yu, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Ziru Liu, Qidong Liu, Hongwei Zhao, Lantao Hu, et al., 2024b. M3oe: Multi-domain multi-task mixture-of experts recommendation framework. In SIGIR. 893-902.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_88_1","first-page":"3453","article-title":"Sequential Recommendation via an Adaptive Cross-domain Knowledge Decomposition","author":"Zhao Chuang","year":"2023","unstructured":"Chuang Zhao, Xinyu Li, Ming He, Hongke Zhao, and Jianping Fan. 2023a. Sequential Recommendation via an Adaptive Cross-domain Knowledge Decomposition. In CIKM. 3453-3463.","journal-title":"CIKM."},{"key":"e_1_3_2_1_89_1","first-page":"2018","article-title":"Distributionally robust graph out-of-distribution recommendation via diffusion model","author":"Zhao Chu","year":"2025","unstructured":"Chu Zhao, Enneng Yang, Yuliang Liang, Jianzhe Zhao, Guibing Guo, and Xingwei Wang. 2025. Distributionally robust graph out-of-distribution recommendation via diffusion model. In WWW. 2018-2031.","journal-title":"WWW."},{"key":"e_1_3_2_1_90_1","first-page":"887","article-title":"Cross-domain recommendation via user interest alignment","author":"Zhao Chuang","year":"2023","unstructured":"Chuang Zhao, Hongke Zhao, Ming He, Jian Zhang, and Jianping Fan. 2023b. Cross-domain recommendation via user interest alignment. In WWW. 887-896.","journal-title":"WWW."},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3437775"},{"key":"e_1_3_2_1_92_1","first-page":"4653","article-title":"Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms","author":"Zhao Wayne Xin","year":"2021","unstructured":"Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, et al., 2021. Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. 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