{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:42:17Z","timestamp":1776400937346,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"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":"publisher","award":["No. 62102420 and No. 61832017"],"award-info":[{"award-number":["No. 62102420 and No. 61832017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China Unicom Innovation Ecological Cooperation Plan, Beijing Outstanding Young Scientist Program","award":["NO. BJJWZYJH012019100020098"],"award-info":[{"award-number":["NO. BJJWZYJH012019100020098"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482305","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T15:31:19Z","timestamp":1636990279000},"page":"2342-2351","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":48,"title":["Top-N Recommendation with Counterfactual User Preference Simulation"],"prefix":"10.1145","author":[{"given":"Mengyue","family":"Yang","sequence":"first","affiliation":[{"name":"University College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanyu","family":"Dai","sequence":"additional","affiliation":[{"name":"Huawei, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhua","family":"Dong","sequence":"additional","affiliation":[{"name":"Huawei, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Big Data Management and Analysis Methods &amp; Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuqiang","family":"He","sequence":"additional","affiliation":[{"name":"Huawei, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"University College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5368"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00466"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240360"},{"key":"e_1_3_2_1_5_1","volume-title":"Bias and Debias in Recommender System: A Survey and Future Directions. arXiv preprint arXiv:2010.03240","author":"Chen Jiawei","year":"2020","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 preprint arXiv:2010.03240 ( 2020 ). 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 preprint arXiv:2010.03240 (2020)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00471"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347058"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3186905"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3411552"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58539-6_5"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220041"},{"key":"e_1_3_2_1_14_1","volume-title":"Counterfactual visual explanations. arXiv preprint arXiv:1904.07451","author":"Goyal Yash","year":"2019","unstructured":"Yash Goyal , Ziyan Wu , Jan Ernst , Dhruv Batra , Devi Parikh , and Stefan Lee . 2019. Counterfactual visual explanations. arXiv preprint arXiv:1904.07451 ( 2019 ). Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Counterfactual visual explanations. arXiv preprint arXiv:1904.07451 (2019)."},{"key":"e_1_3_2_1_15_1","volume-title":"Deepfm: An end-to-end wide & deep learning framework for CTR prediction. arXiv preprint arXiv:1804.04950","author":"Guo Huifeng","year":"2018","unstructured":"Huifeng Guo , Ruiming Tang , Yunming Ye , Zhenguo Li , Xiuqiang He , and Zhenhua Dong . 2018 . Deepfm: An end-to-end wide & deep learning framework for CTR prediction. arXiv preprint arXiv:1804.04950 (2018). Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, and Zhenhua Dong. 2018. Deepfm: An end-to-end wide & deep learning framework for CTR prediction. arXiv preprint arXiv:1804.04950 (2018)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3209981"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_19_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds .). Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401083"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159728"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"e_1_3_2_1_23_1","unstructured":"Judea Pearl. 2009. Causality. Cambridge university press.  Judea Pearl. 2009. Causality. Cambridge university press."},{"key":"e_1_3_2_1_24_1","volume-title":"Causal inference in statistics: A primer","author":"Pearl Judea","unstructured":"Judea Pearl , Madelyn Glymour , and Nicholas P Jewell . 2016. Causal inference in statistics: A primer . John Wiley & Sons . Judea Pearl, Madelyn Glymour, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer. John Wiley & Sons."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/3202377"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/1795114.1795167"},{"key":"e_1_3_2_1_27_1","volume-title":"NeurIPS 2019 Workshop on Causal Machine Learning.","author":"Saito Yuta","year":"2019","unstructured":"Yuta Saito . 2019 . Unbiased Pairwise Learning from Implicit Feedback . In NeurIPS 2019 Workshop on Causal Machine Learning. Yuta Saito. 2019. Unbiased Pairwise Learning from Implicit Feedback. In NeurIPS 2019 Workshop on Causal Machine Learning."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045567"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/2621980"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148257"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080786"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3124749.3124754"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462855"},{"key":"e_1_3_2_1_35_1","volume-title":"MIND: A Large-scale Dataset for News Recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020","author":"Wu Fangzhao","year":"2020","unstructured":"Fangzhao Wu , Ying Qiao , Jiun-Hung Chen , Chuhan Wu , Tao Qi , Jianxun Lian , Danyang Liu , Xing Xie , Jianfeng Gao , Winnie Wu , and Ming Zhou . [n.d.]. MIND: A Large-scale Dataset for News Recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 , Online , July 5-10, 2020 . 3597--3606. Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, and Ming Zhou. [n.d.]. MIND: A Large-scale Dataset for News Recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020. 3597--3606."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835837"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240355"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939673"},{"key":"e_1_3_2_1_39_1","volume-title":"RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. CoRR","author":"Zhao Wayne Xin","year":"2020","unstructured":"Wayne Xin Zhao , Shanlei Mu , Yupeng Hou , Zihan Lin , Kaiyuan Li , Yushuo Chen , Yujie Lu , Hui Wang , Changxin Tian , Xingyu Pan , Yingqian Min , Zhichao Feng , Xinyan Fan , Xu Chen , Pengfei Wang , Wendi Ji , Yaliang Li , Xiaoling Wang , and Ji-Rong Wen . 2020. RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. CoRR , Vol. abs\/ 2011 .01731 ( 2020 ). arxiv: 2011.01731 https:\/\/arxiv.org\/abs\/2011.01731 Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Kaiyuan Li, Yushuo Chen, Yujie Lu, Hui Wang, Changxin Tian, Xingyu Pan, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, and Ji-Rong Wen. 2020. RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. CoRR, Vol. abs\/2011.01731 (2020). arxiv: 2011.01731 https:\/\/arxiv.org\/abs\/2011.01731"},{"key":"e_1_3_2_1_40_1","volume-title":"Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209","author":"Zhao Xiangyu","year":"2017","unstructured":"Xiangyu Zhao , Liang Zhang , Long Xia , Zhuoye Ding , Dawei Yin , and Jiliang Tang . 2017. Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209 ( 2017 ). Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2017. Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209 (2017)."},{"key":"e_1_3_2_1_41_1","volume-title":"Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571","author":"Zmigrod Ran","year":"2019","unstructured":"Ran Zmigrod , Sabrina J Mielke , Hanna Wallach , and Ryan Cotterell . 2019. Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571 ( 2019 ). Ran Zmigrod, Sabrina J Mielke, Hanna Wallach, and Ryan Cotterell. 2019. Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571 (2019)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330852"}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia","acronym":"CIKM '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482305","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482305","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:13Z","timestamp":1750191133000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":42,"alternative-id":["10.1145\/3459637.3482305","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482305","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}