{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T01:31:46Z","timestamp":1779327106634,"version":"3.51.4"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T00:00:00Z","timestamp":1724198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62032013"],"award-info":[{"award-number":["62032013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and technology projects in Liaoning Province","award":["2023JH3\/10200005"],"award-info":[{"award-number":["2023JH3\/10200005"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["N2317002"],"award-info":[{"award-number":["N2317002"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>Recommender systems are influenced by many confounding factors (i.e., confounders) which result in various biases (e.g., popularity biases) and inaccurate user preference. Existing approaches try to eliminate these biases by inference with causal graphs. However, they assume all confounding factors can be observed and no hidden confounders exist. We argue that many confounding factors (e.g., season) may not be observable from user\u2013item interaction data, resulting inaccurate user preference. In this article, we propose a deconfounded recommender considering unobservable confounders. Specifically, we propose a new causal graph with explicit and implicit feedback, which can better model user preference. Then, we realize a deconfounded estimator by the front-door adjustment, which is able to eliminate the effect of unobserved confounders. Finally, we conduct a series of experiments on two real-world datasets, and the results show that our approach performs better than other counterparts in terms of recommendation accuracy.<\/jats:p>","DOI":"10.1145\/3673762","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T07:59:51Z","timestamp":1718697591000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Deconfounding User Preference in Recommendation Systems through Implicit and Explicit Feedback"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2946-7910","authenticated-orcid":false,"given":"Yuliang","family":"Liang","sequence":"first","affiliation":[{"name":"Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5419-5286","authenticated-orcid":false,"given":"Enneng","family":"Yang","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1709-5056","authenticated-orcid":false,"given":"Guibing","family":"Guo","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0704-6621","authenticated-orcid":false,"given":"Wei","family":"Cai","sequence":"additional","affiliation":[{"name":"Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7492-0473","authenticated-orcid":false,"given":"Linying","family":"Jiang","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2856-4716","authenticated-orcid":false,"given":"Xingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"21","volume-title":"Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Chen Jiawei","year":"2021","unstructured":"Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, and Keping Yang. 2021. AutoDebias: Learning to debias for recommendation. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. 21\u201330."},{"issue":"3","key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3564284","article-title":"Bias and debias in recommender system: A survey and future directions","volume":"41","author":"Chen Jiawei","year":"2023","unstructured":"Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems (TOIS) 41, 3 (2023), 1\u201339.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"issue":"6","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"2686","DOI":"10.1109\/TKDE.2023.3324312","article-title":"TiCoSeRec: Augmenting data to uniform sequences by time intervals for effective recommendation","volume":"36","author":"Dang Yizhou","year":"2023","unstructured":"Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, and Hong Liu. 2023. TiCoSeRec: Augmenting data to uniform sequences by time intervals for effective recommendation. IEEE Transactions on Knowledge and Data Engineering 36, 6 (2023), 2686\u20132700.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_5_2","first-page":"1623","volume-title":"Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM)","author":"Gao Chen","year":"2022","unstructured":"Chen Gao, Xiang Wang, Xiangnan He, and Yong Li. 2022. Graph neural networks for recommender system. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM). 1623\u20131625."},{"key":"e_1_3_1_6_2","volume-title":"Causal Inference in Statistics: A Primer","author":"Glymour Madelyn","year":"2016","unstructured":"Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. John Wiley & Sons."},{"key":"e_1_3_1_7_2","first-page":"639","volume-title":"ACM SIGIR Conference on Research and Development in Information Retrieval","author":"He Xiangnan","year":"2020","unstructured":"Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In ACM SIGIR Conference on Research and Development in Information Retrieval. 639\u2013648."},{"key":"e_1_3_1_8_2","first-page":"173","volume-title":"Proceedings of the International Conference on World Wide Web (WWW)","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the International Conference on World Wide Web (WWW). 173\u2013182."},{"key":"e_1_3_1_9_2","first-page":"652","volume-title":"Proceedings of Machine Learning Research (PMLR)","author":"Jiang Nan","year":"2016","unstructured":"Nan Jiang and Lihong Li. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of Machine Learning Research (PMLR). 652\u2013661."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_1_12_2","first-page":"351","volume-title":"Proceedings of the ACM Conference on Recommender Systems (RecSys)","author":"Liu Dugang","year":"2021","unstructured":"Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming. 2021. Mitigating confounding bias in recommendation via information bottleneck. In Proceedings of the ACM Conference on Recommender Systems (RecSys). 351\u2013360."},{"key":"e_1_3_1_13_2","first-page":"012008","volume-title":"Journal of Physics: Conference Series","volume":"2004","author":"Ma Zhe","year":"2021","unstructured":"Zhe Ma and Qiang Dong. 2021. Alleviating the unfairness of recommendation by eliminating the conformity bias. Journal of Physics: Conference Series 2004, 1 (2021), 012008."},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/1639714.1639717","volume-title":"Proceedings of the ACM Conference on Recommender Systems (RecSys)","author":"Marlin Benjamin M.","year":"2009","unstructured":"Benjamin M. Marlin and Richard S. Zemel. 2009. Collaborative prediction and ranking with non-random missing data. In Proceedings of the ACM Conference on Recommender Systems (RecSys). 5\u201312."},{"key":"e_1_3_1_15_2","unstructured":"Aaron van den Oord Yazhe Li and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv:1807.03748. Retrieved from https:\/\/arxiv.org\/abs\/1807.03748"},{"key":"e_1_3_1_16_2","first-page":"309","volume-title":"Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Saito Yuta","year":"2020","unstructured":"Yuta Saito. 2020. Asymmetric tri-training for debiasing missing-not-at-random explicit feedback. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. 309\u2013318."},{"key":"e_1_3_1_17_2","first-page":"1670","volume-title":"Proceedings of Machine Learning Research (PMLR)","author":"Schnabel Tobias","year":"2016","unstructured":"Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In Proceedings of Machine Learning Research (PMLR). 1670\u20131679."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3582435"},{"issue":"6","key":"e_1_3_1_19_2","first-page":"142:1","article-title":"Multi-scenario and multi-task aware feature interaction for recommendation system","volume":"18","author":"Song Derun","year":"2024","unstructured":"Derun Song, Enneng Yang, Guibing Guo, Li Shen, Linying Jiang, and Xingwei Wang. 2024. Multi-scenario and multi-task aware feature interaction for recommendation system. ACM Transactions on Knowledge Discovery from Data 18, 6 (2024), 142:1\u2013142:20.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_20_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)","volume":"28","author":"Swaminathan Adith","year":"2015","unstructured":"Adith Swaminathan and Thorsten Joachims. 2015. The self-normalized estimator for counterfactual learning. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vol. 28."},{"issue":"86","key":"e_1_3_1_21_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten Laurens Van der","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. JMLR 9, 86 (2008), 2579\u20132605.","journal-title":"JMLR"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1145\/3447548.3467249","volume-title":"Proceedings of the ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Wang Wenjie","year":"2021","unstructured":"Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, and Tat-Seng Chua. 2021. Deconfounded recommendation for alleviating bias amplification. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1717\u20131725."},{"key":"e_1_3_1_23_2","first-page":"528","article-title":"The blessings of multiple causes","volume":"114","author":"Wang Yixin","year":"2019","unstructured":"Yixin Wang and David M. Blei. 2019. The blessings of multiple causes. Journal of the American Statistical Association 114, 528 (2019), 1574\u20131596.","journal-title":"Journal of the American Statistical Association"},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1145\/3383313.3412225","volume-title":"Proceedings of the ACM Conference on Recommender Systems (RecSys)","author":"Wang Yixin","year":"2020","unstructured":"Yixin Wang, Dawen Liang, Laurent Charlin, and David M. Blei. 2020b. Causal inference for recommender systems. In Proceedings of the ACM Conference on Recommender Systems (RecSys). 426\u2013431."},{"key":"e_1_3_1_25_2","first-page":"1854","volume-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)","author":"Wang Zifeng","year":"2020","unstructured":"Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu, and Yefeng Zheng. 2020a. Information theoretic counterfactual learning from missing-not-at-random feedback. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 1854\u20131864."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3606035"},{"key":"e_1_3_1_27_2","first-page":"2342","volume-title":"Proceedings of the ACM International Conference on Information & Knowledge Management (CIKM)","author":"Yang Mengyue","year":"2021","unstructured":"Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, and Jun Wang. 2021. Top-N recommendation with counterfactual user preference simulation. In Proceedings of the ACM International Conference on Information & Knowledge Management (CIKM). 2342\u20132351."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3444944"},{"key":"e_1_3_1_29_2","first-page":"11","volume-title":"Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhang Yang","year":"2021","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 Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. 11\u201320."},{"issue":"10","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"9920","DOI":"10.1109\/TKDE.2022.3218994","article-title":"Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation","volume":"35","author":"Zhao Zihao","year":"2021","unstructured":"Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, and Wei Wu. 2021. Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation. IEEE Transactions on Knowledge and Data Engineering (TKDE) 35, 10 (2021), 9920\u20139931.","journal-title":"IEEE Transactions on Knowledge and Data Engineering (TKDE)"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3378482"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3673762","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3673762","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:08Z","timestamp":1750291568000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3673762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,21]]},"references-count":30,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3673762"],"URL":"https:\/\/doi.org\/10.1145\/3673762","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,21]]},"assertion":[{"value":"2023-07-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}