{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:45:28Z","timestamp":1792799128181,"version":"4.5.0"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Starry Night Science Fund of Zhejiang University Shanghai Institute","award":["SN-ZJU-SIAS-001"],"award-info":[{"award-number":["SN-ZJU-SIAS-001"]}],"id":[{"id":"https:\/\/ror.org\/00a2xv884","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372399"],"award-info":[{"award-number":["62372399"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n                    Implicit feedback (e.g., user clicks) is widely used in building recommender systems (RS). However, the inherent notorious\n                    <jats:italic>exposure bias<\/jats:italic>\n                    significantly affects recommendation performance. Exposure bias refers a phenomenon that implicit feedback is influenced by user exposure and does not precisely reflect user preference. Current methods for addressing exposure bias primarily reduce confidence in unclicked data, employ exposure models, or leverage propensity scores. Regrettably, these approaches often lead to biased estimations or elevated model variance, yielding sub-optimal results.\n                  <\/jats:p>\n                  <jats:p>\n                    To overcome these limitations, we propose a new method\n                    <jats:bold>ReCRec<\/jats:bold>\n                    that\n                    <jats:bold>\n                      Reasons the\n                      <jats:italic>C<\/jats:italic>\n                      auses behind the implicit feedback for debiased R\n                      <jats:italic>ec<\/jats:italic>\n                      ommendation\n                    <\/jats:bold>\n                    . ReCRec identifies three scenarios behind unclicked data\u2014i.e., unexposed, dislike, or a combination of both. A reasoning module is employed to infer the category to which each instance pertains. Consequently, the model is capable of extracting reliable positive and negative signals from unclicked data, thereby facilitating more accurate learning of user preferences. We also conduct thorough theoretical analyses to demonstrate the debiased nature and low variance of ReCRec. Extensive experiments on both semi-synthetic and real-world datasets validate its superiority over state-of-the-art methods.\n                  <\/jats:p>","DOI":"10.1145\/3672275","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T12:03:43Z","timestamp":1720440223000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["ReCRec: Reasoning the Causes of Implicit Feedback for Debiased Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2710-6169","authenticated-orcid":false,"given":"Siyi","family":"Lin","sequence":"first","affiliation":[{"name":"College of Computer Science, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3645-1041","authenticated-orcid":false,"given":"Sheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Software Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4752-2629","authenticated-orcid":false,"given":"Jiawei","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3605-5404","authenticated-orcid":false,"given":"Yan","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China and College of Computer Science, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7883-9848","authenticated-orcid":false,"given":"Qihao","family":"Shi","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6198-7481","authenticated-orcid":false,"given":"Chun","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China and College of Computer Science, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1393-2344","authenticated-orcid":false,"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Bangsun Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-4307","authenticated-orcid":false,"given":"Can","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Rianne van den Berg Thomas N. Kipf and Max Welling. 2017. Graph convolutional matrix completion. arXiv:1706.02263. Retrieved from https:\/\/arxiv.org\/abs\/1706.02263"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240360"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462919"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","unstructured":"Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. arXiv:2010.03240. Retrieved from 10.48550\/arXiv.2010.03240","DOI":"10.48550\/arXiv.2010.03240"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271742"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5751"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313582"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583341"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290999"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657747"},{"key":"e_1_3_3_13_2","unstructured":"Andrew Collins Dominika Tkaczyk Akiko Aizawa and Joeran Beel. 2018. A study of position bias in digital library recommender systems. arXiv:1802.06565. Retrieved from https:\/\/arxiv.org\/abs\/1802.06565"},{"key":"e_1_3_3_14_2","doi-asserted-by":"crossref","unstructured":"Yu Cui Feng Liu Pengbo Wang Bohao Wang Heng Tang Yi Wan Jun Wang and Jiawei Chen. 2024. Distillation matters: Empowering sequential recommenders to match the performance of large language model. arXiv:2405.00338. Retrieved from https:\/\/arxiv.org\/abs\/2405.00338","DOI":"10.1145\/3640457.3688118"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"e_1_3_3_16_2","first-page":"3900","article-title":"Graph convolution network based recommender systems: Learning guarantee and item mixture powered strategy","volume":"35","author":"Deng Leyan","year":"2022","unstructured":"Leyan Deng, Defu Lian, Chenwang Wu, and Enhong Chen. 2022. Graph convolution network based recommender systems: Learning guarantee and item mixture powered strategy. Advances in Neural Information Processing Systems 35 (2022), 3900\u20133912.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449927"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591636"},{"key":"e_1_3_3_19_2","article-title":"CIRS: Bursting filter bubbles by counterfactual interactive recommender system","author":"Gao Chongming","year":"2022","unstructured":"Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, and Peng Jiang. 2022. CIRS: Bursting filter bubbles by counterfactual interactive recommender system. ACM Transactions on Information Systems (2022).","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3568022"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557314"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.5555\/3304889.3304969"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911489"},{"key":"e_1_3_3_26_2","first-page":"1512","volume-title":"International Conference on Machine Learning","author":"Hern\u00e1ndez-Lobato Jos\u00e9 Miguel","year":"2014","unstructured":"Jos\u00e9 Miguel Hern\u00e1ndez-Lobato, Neil Houlsby, and Zoubin Ghahramani. 2014. Probabilistic matrix factorization with non-random missing data. In International Conference on Machine Learning. PMLR, 1512\u20131520."},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv:1511.06939. Retrieved from 10.48550\/arXiv.1511.06939","DOI":"10.48550\/arXiv.1511.06939"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052639"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_3_30_2","unstructured":"Wenyue Hua Yingqiang Ge Shuyuan Xu Jianchao Ji and Yongfeng Zhang. 2023. UP5: Unbiased foundation model for fairness-aware recommendation. arXiv:2305.12090. Retrieved from https:\/\/arxiv.org\/abs\/2305.12090"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3130332.3130334"},{"issue":"78","key":"e_1_3_3_32_2","first-page":"1","article-title":"Logistic matrix factorization for implicit feedback data","volume":"27","author":"Johnson Christopher C.","year":"2014","unstructured":"Christopher C. Johnson. 2014. Logistic matrix factorization for implicit feedback data. Advances in Neural Information Processing Systems 27, 78 (2014), 1\u20139.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463118"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531946"},{"issue":"1","key":"e_1_3_3_37_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3533725","article-title":"Be causal: De-biasing social network confounding in recommendation","volume":"17","author":"Li Qian","year":"2023","unstructured":"Qian Li, Xiangmeng Wang, Zhichao Wang, and Guandong Xu. 2023. Be causal: De-biasing social network confounding in recommendation. ACM Transactions on Knowledge Discovery from Data 17, 1 (2023), 1\u201323.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","unstructured":"Yunqi Li Hanxiong Chen Shuyuan Xu Yingqiang Ge Juntao Tan Shuchang Liu and Yongfeng Zhang. 2022. Fairness in recommendation: A survey. arXiv:2205.13619. Retrieved from 10.48550\/arXiv.2205.13619","DOI":"10.48550\/arXiv.2205.13619"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883090"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","unstructured":"Siyi Lin Chongming Gao Jiawei Chen Sheng Zhou Binbin Hu and Can Wang. 2024. How do recommendation models amplify popularity bias? An analysis from the spectral perspective. arXiv:2404.12008. Retrieved from 10.48550\/arXiv.2404.12008","DOI":"10.48550\/arXiv.2404.12008"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401083"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959141"},{"key":"e_1_3_3_44_2","first-page":"5712","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems 2019 (NeurIPS \u201919)","author":"Ma Jianxin","year":"2019","unstructured":"Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019. Learning disentangled representations for recommendation. In Proceedings of the 33rd International Conference on Neural Information Processing Systems 2019 (NeurIPS \u201919). Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d\u2019Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett (Eds.), 5712\u20135723. DOI: https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/a2186aa7c086b46ad4e8bf81e2a3a19b-Abstract.html"},{"key":"e_1_3_3_45_2","first-page":"267","volume-title":"Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence","author":"Marlin Benjamin M.","year":"2007","unstructured":"Benjamin M. Marlin, Richard S. Zemel, Sam Roweis, and Malcolm Slaney. 2007. Collaborative filtering and the missing at random assumption. In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence. 267\u2013275."},{"key":"e_1_3_3_46_2","first-page":"1881","volume-title":"Proceedings of the 28th Annual Conference of the Cognitive Science Society","volume":"28","author":"O\u2019Brien Maeve","year":"2006","unstructured":"Maeve O\u2019Brien and Mark T. Keane. 2006. Modeling result-list searching in the World Wide Web: The role of relevance topologies and trust bias. In Proceedings of the 28th Annual Conference of the Cognitive Science Society, Vol. 28, Citeseer, 1881\u20131886."},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557094"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.16"},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403285"},{"key":"e_1_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475266"},{"key":"e_1_3_3_51_2","doi-asserted-by":"publisher","unstructured":"Steffen Rendle Christoph Freudenthaler Zeno Gantner and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618. Retrieved from 10.48550\/arXiv.1205.2618","DOI":"10.48550\/arXiv.1205.2618"},{"key":"e_1_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3409256.3409812"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371783"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742726"},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313710"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186154"},{"key":"e_1_3_3_58_2","unstructured":"A\u00e4ron van den Oord Yazhe Li and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv:1807.03748. Retrieved from http:\/\/arxiv.org\/abs\/1807.03748"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645598"},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570483"},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11835"},{"key":"e_1_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1089\/big.2014.0063"},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_3_65_2","article-title":"Causal disentanglement for semantics-aware intent learning in recommendation","author":"Wang Xiangmeng","year":"2022","unstructured":"Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, and Guandong Xu. 2022. Causal disentanglement for semantics-aware intent learning in recommendation. IEEE Transactions on Knowledge and Data Engineering (2022).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_3_66_2","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Wu Junkang","year":"2024","unstructured":"Junkang Wu, Jiawei Chen, Jiancan Wu, Wentao Shi, Xiang Wang, and Xiangnan He. 2024. Understanding contrastive learning via distributionally robust optimization. In Proceedings of the 37th International Conference on Neural Information Processing Systems."},{"key":"e_1_3_3_67_2","unstructured":"Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian and Xing Xie. 2020b. Self-supervised graph learning for recommendation. arXiv:2010.10783. Retrieved from https:\/\/arxiv.org\/abs\/2010.10783"},{"issue":"5","key":"e_1_3_3_68_2","first-page":"4425","article-title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation","volume":"35","author":"Wu Le","year":"2022","unstructured":"Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 4425\u20134445.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_3_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_3_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240355"},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583361"},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.01.089"},{"key":"e_1_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531937"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3282907"},{"key":"e_1_3_3_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462875"},{"key":"e_1_3_3_76_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449788"},{"key":"e_1_3_3_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412210"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3672275","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3672275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:57:49Z","timestamp":1750280269000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3672275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"references-count":76,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,11,30]]}},"alternative-id":["10.1145\/3672275"],"URL":"https:\/\/doi.org\/10.1145\/3672275","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,18]]},"assertion":[{"value":"2023-07-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}