{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:02:07Z","timestamp":1775228527129,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["9227010114, U21B2026"],"award-info":[{"award-number":["9227010114, U21B2026"]}]},{"name":"the University Synergy Innovation Program of Anhui Province","award":["GXXT-2022-040"],"award-info":[{"award-number":["GXXT-2022-040"]}]},{"name":"the National Key Research and Development Program of China","award":["2021ZD0111802"],"award-info":[{"award-number":["2021ZD0111802"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,7,19]]},"DOI":"10.1145\/3539618.3591624","type":"proceedings-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:22:59Z","timestamp":1689726179000},"page":"331-340","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["A Generic Learning Framework for Sequential Recommendation with Distribution Shifts"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8094-0978","authenticated-orcid":false,"given":"Zhengyi","family":"Yang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8472-7992","authenticated-orcid":false,"given":"Xiangnan","family":"He","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0251-465X","authenticated-orcid":false,"given":"Jizhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6941-5218","authenticated-orcid":false,"given":"Jiancan","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-9115","authenticated-orcid":false,"given":"Xin","family":"Xin","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7054-7974","authenticated-orcid":false,"given":"Jiawei","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6148-6329","authenticated-orcid":false,"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1120.1641"},{"key":"e_1_3_2_1_2_1","volume-title":"Santos","author":"Vecchia Chaves Pedro Dalla","year":"2022","unstructured":"Pedro Dalla Vecchia Chaves, Bruno L. Pereira, and Rodrygo L. T. Santos. 2022. Efficient Online Learning to Rank for Sequential Music Recommendation. In WWW. 2442--2450."},{"key":"e_1_3_2_1_3_1","volume-title":"Efficient Neural Matrix Factorization without Sampling for Recommendation. TOIS 38","author":"Chen Chong","year":"2020","unstructured":"Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient Neural Matrix Factorization without Sampling for Recommendation. TOIS 38 (2020), 14:1--14:28."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Yongjun Chen Zhiwei Liu Jia Li Julian J. McAuley and Caiming Xiong. 2022. Intent Contrastive Learning for Sequential Recommendation. In WWW. 2172--2182.","DOI":"10.1145\/3485447.3512090"},{"key":"e_1_3_2_1_5_1","unstructured":"Chen Cheng Haiqin Yang Michael R Lyu and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In IJCAI. 2605--2611."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198.","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_7_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186.","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Sihao Ding Fuli Feng Xiangnan He Jinqiu Jin Wenjie Wang Yong Liao and Yongdong Zhang. 2022. Interpolative Distillation for Unifying Biased and Debiased Recommendation. In SIGIR. 40--49.","DOI":"10.1145\/3477495.3532002"},{"key":"e_1_3_2_1_9_1","volume-title":"Zheng Liu, Chaozhuo Li, and Xing Xie.","author":"Fan Xinyan","year":"2022","unstructured":"Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li, and Xing Xie. 2022. Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation. In SIGIR. 1599--1610."},{"key":"e_1_3_2_1_10_1","volume-title":"Xing Xie, and Ji-Rong Wen.","author":"Fan Xinyan","year":"2021","unstructured":"Xinyan Fan, Zheng Liu, Jianxun Lian, Wayne Xin Zhao, Xing Xie, and Ji-Rong Wen. 2021. Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation. In SIGIR. 1733--1737."},{"key":"e_1_3_2_1_11_1","unstructured":"Chongming Gao Shijun Li Wenqiang Lei Jiawei Chen Biao Li Peng Jiang Xiangnan He Jiaxin Mao and Tat-Seng Chua. 2022. KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems. In CIKM."},{"key":"e_1_3_2_1_12_1","volume-title":"Girshick","author":"He Kaiming","year":"2022","unstructured":"Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll\u00e1r, and Ross B. Girshick. 2022. Masked Autoencoders Are Scalable Vision Learners. In CVPR. 15979--15988."},{"key":"e_1_3_2_1_13_1","unstructured":"Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In ICDM. 191--200."},{"key":"e_1_3_2_1_14_1","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In ICLR."},{"key":"e_1_3_2_1_15_1","volume-title":"Kullback-Leibler divergence constrained distributionally robust optimization. Available at Optimization Online","author":"Hu Zhaolin","year":"2013","unstructured":"Zhaolin Hu and L Jeff Hong. 2013. Kullback-Leibler divergence constrained distributionally robust optimization. Available at Optimization Online (2013), 1695--1724."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recom- mendation. In ICDM. 197--206.","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_17_1","first-page":"5815","article-title":"Out-of- Distribution Generalization via Risk Extrapolation (REx)","volume":"139","author":"Krueger David","year":"2021","unstructured":"David Krueger, Ethan Caballero, J\u00f6rn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, R\u00e9mi Le Priol, and Aaron C. Courville. 2021. Out-of- Distribution Generalization via Risk Extrapolation (REx). In ICML, Vol. 139. 5815--5826.","journal-title":"ICML"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/3219358.3219362"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3934\/naco.2021057"},{"key":"e_1_3_2_1_20_1","unstructured":"Divyat Mahajan Shruti Tople and Amit Sharma. 2021. Domain generalization using causal matching. In ICML. 7313--7324."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.7153\/mia-01-05"},{"key":"e_1_3_2_1_22_1","volume-title":"Foundations of Machine Learning","author":"Mohri Mehryar","unstructured":"Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2012. Foundations of Machine Learning. MIT Press."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Yabo Ni Dan Ou Shichen Liu Xiang Li Wenwu Ou Anxiang Zeng and Luo Si. 2018. Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks. In SIGKDD. 596--605.","DOI":"10.1145\/3219819.3219828"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Yonatan Oren Shiori Sagawa Tatsunori B. Hashimoto and Percy Liang. 2019. Distributionally Robust Language Modeling. In EMNLP. 4226--4236.","DOI":"10.18653\/v1\/D19-1432"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Aleksandr Petrov and Craig Macdonald. 2022. Effective and Efficient Training for Sequential Recommendation using Recency Sampling. In RecSys. ACM 81--91.","DOI":"10.1145\/3523227.3546785"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Ruihong Qiu Zi Huang Hongzhi Yin and Zijian Wang. 2022. Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. In WSDM K. Selcuk Candan Huan Liu Leman Akoglu Xin Luna Dong and Jiliang Tang (Eds.). 813--823.","DOI":"10.1145\/3488560.3498433"},{"key":"e_1_3_2_1_27_1","volume-title":"BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. 452-- 461.","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. 452-- 461."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle Christoph Freudenthaler and Lars Schmidt-Thieme. 2010. Factor- izing personalized markov chains for next-basket recommendation. In WWW. 811--820.","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_1_29_1","first-page":"20210","article-title":"Model-based domain generalization","volume":"34","author":"Robey Alexander","year":"2021","unstructured":"Alexander Robey, George J Pappas, and Hamed Hassani. 2021. Model-based domain generalization. NeurIPS 34 (2021), 20210--20229.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_30_1","volume-title":"Tatsunori B. Hashimoto, and Percy Liang.","author":"Sagawa Shiori","year":"2020","unstructured":"Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang. 2020. Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization. In ICLR."},{"key":"e_1_3_2_1_31_1","unstructured":"Tobias Schnabel Adith Swaminathan Ashudeep Singh Navin Chandak and Thorsten Joachims. 2016. Recommendations as Treatments: Debiasing Learning and Evaluation. In ICML. JMLR.org 1670--1679."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Fei Sun Jun Liu Jian Wu Changhua Pei Xiao Lin Wenwu Ou and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In CIKM. 1441--1450.","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_1_33_1","unstructured":"Qiaoyu Tan Jianwei Zhang Jiangchao Yao Ninghao Liu Jingren Zhou Hongxia Yang and Xia Hu. 2021. Sparse-Interest Network for Sequential Recommendation. In WSDM. 598--606."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM. 565--573.","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_1_35_1","unstructured":"Riccardo Volpi Hongseok Namkoong Ozan Sener John C. Duchi Vittorio Murino and Silvio Savarese. 2018. Generalizing to Unseen Domains via Adversarial Data Augmentation. In NeurIPS. 5339--5349."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Qi Wan Xiangnan He Xiang Wang Jiancan Wu Wei Guo and Ruiming Tang. 2022. Cross Pairwise Ranking for Unbiased Item Recommendation. In WWW. 2370--2378.","DOI":"10.1145\/3485447.3512010"},{"key":"e_1_3_2_1_37_1","volume-title":"Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors","author":"Wang Xuejian","year":"2051","unstructured":"Xuejian Wang, Lantao Yu, Kan Ren, Guanyu Tao, Weinan Zhang, Yong Yu, and Jun Wang. 2017. Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration. In SIGKDD. 2051--2059."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Zimu Wang Yue He Jiashuo Liu Wenchao Zou Philip S. Yu and Peng Cui. 2022. Invariant Preference Learning for General Debiasing in Recommendation. In SIGKDD. 1969--1978.","DOI":"10.1145\/3534678.3539439"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Zijian Wang Yadan Luo Ruihong Qiu Zi Huang and Mahsa Baktashmotlagh. 2021. Learning to Diversify for Single Domain Generalization. In ICCV. 814--823.","DOI":"10.1109\/ICCV48922.2021.00087"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Zhenlei Wang Shiqi Shen Zhipeng Wang Bo Chen Xu Chen and Ji-Rong Wen. 2022. Unbiased Sequential Recommendation with Latent Confounders. In WWW. 2195--2204.","DOI":"10.1145\/3485447.3512092"},{"key":"e_1_3_2_1_41_1","volume-title":"Chi","author":"Wen Hongyi","year":"2022","unstructured":"Hongyi Wen, Xinyang Yi, Tiansheng Yao, Jiaxi Tang, Lichan Hong, and Ed H. Chi. 2022. Distributionally-robust Recommendations for Improving Worst-case User Experience. In WWW. 3606--3610."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In SIGIR. ACM 726--735.","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_1_43_1","volume-title":"On the Effectiveness of Sampled Softmax Loss for Item Recommendation. CoRR abs\/2201.02327","author":"Wu Jiancan","year":"2022","unstructured":"Jiancan Wu, Xiang Wang, Xingyu Gao, Jiawei Chen, Hongcheng Fu, Tianyu Qiu, and Xiangnan He. 2022. On the Effectiveness of Sampled Softmax Loss for Item Recommendation. CoRR abs\/2201.02327 (2022)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","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. 1259--1273.","DOI":"10.1109\/ICDE53745.2022.00099"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Fajie Yuan Xiangnan He Alexandros Karatzoglou and Liguang Zhang. 2020. Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. In SIGIR. 1469--1478.","DOI":"10.1145\/3397271.3401156"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Fajie Yuan Alexandros Karatzoglou Ioannis Arapakis Joemon M. Jose and Xiangnan He. 2019. A Simple Convolutional Generative Network for Next Item Recommendation. In WSDM. 582--590.","DOI":"10.1145\/3289600.3290975"},{"key":"e_1_3_2_1_47_1","unstructured":"An Zhang Wenchang Ma Xiang Wang and Tat seng Chua. 2022. Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering. In NeurIPS."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"An Zhang Jingnan Zheng Xiang Wang Yancheng Yuan and Tat seng Chua. 2023. Invariant Collaborative Filtering to Popularity Distribution Shift. In WWW.","DOI":"10.1145\/3543507.3583461"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Yang Zhang Fuli Feng Xiangnan He Tianxin Wei Chonggang Song Guohui Ling and Yongdong Zhang. 2021. Causal Intervention for Leveraging Popularity Bias in Recommendation. In SIGIR. 11--20.","DOI":"10.1145\/3404835.3462875"},{"key":"e_1_3_2_1_50_1","volume-title":"Metaxas","author":"Zhao Long","year":"2020","unstructured":"Long Zhao, Ting Liu, Xi Peng, and Dimitris N. Metaxas. 2020. Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness. In NeurIPS."},{"key":"e_1_3_2_1_51_1","unstructured":"Shanshan Zhao Mingming Gong Tongliang Liu Huan Fu and Dacheng Tao. 2020. Domain Generalization via Entropy Regularization. In NeurIPS."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Yu Zheng Chen Gao Xiang Li Xiangnan He Yong Li and Depeng Jin. 2021. Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In WWW. 2980--2991.","DOI":"10.1145\/3442381.3449788"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Guorui Zhou Xiaoqiang Zhu Chengru Song Ying Fan Han Zhu Xiao Ma Yanghui Yan Junqi Jin Han Li and Kun Gai. 2018. Deep Interest Network for Click-Through Rate Prediction. In SIGKDD. 1059--1068","DOI":"10.1145\/3219819.3219823"}],"event":{"name":"SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Taipei Taiwan","acronym":"SIGIR '23","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3591624","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539618.3591624","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:40Z","timestamp":1750182700000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3591624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,18]]},"references-count":53,"alternative-id":["10.1145\/3539618.3591624","10.1145\/3539618"],"URL":"https:\/\/doi.org\/10.1145\/3539618.3591624","relation":{},"subject":[],"published":{"date-parts":[[2023,7,18]]},"assertion":[{"value":"2023-07-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}