{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T19:07:56Z","timestamp":1773515276739,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,20]]},"DOI":"10.1145\/3529372.3530921","type":"proceedings-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T20:57:52Z","timestamp":1654549072000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Causal factorization machine for robust recommendation"],"prefix":"10.1145","author":[{"given":"Yunqi","family":"Li","sequence":"first","affiliation":[{"name":"Rutgers University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanxiong","family":"Chen","sequence":"additional","affiliation":[{"name":"Rutgers University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juntao","family":"Tan","sequence":"additional","affiliation":[{"name":"Rutgers University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rutgers University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Invariant risk minimization. arXiv preprint arXiv:1907.02893","author":"Arjovsky Martin","year":"2019","unstructured":"Martin Arjovsky , L\u00e9on Bottou , Ishaan Gulrajani , and David Lopez-Paz . 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 ( 2019 ). Martin Arjovsky, L\u00e9on Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12268"},{"key":"e_1_3_2_1_3_1","volume-title":"An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research 46, 3","author":"Austin Peter C","year":"2011","unstructured":"Peter C Austin . 2011. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research 46, 3 ( 2011 ), 399--424. Peter C Austin. 2011. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research 46, 3 (2011), 399--424."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2005.00377.x"},{"key":"e_1_3_2_1_5_1","unstructured":"Mathieu Blondel Akinori Fujino Naonori Ueda and Masakazu Ishihata. 2016. Higher-order factorization machines. In NIPS. 3351--3359. Mathieu Blondel Akinori Fujino Naonori Ueda and Masakazu Ishihata. 2016. Higher-order factorization machines. In NIPS. 3351--3359."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Hanxiong Chen Yunqi Li Shaoyun Shi Shuchang Liu He Zhu and Yongfeng Zhang. 2022. Graph collaborative reasoning. In WSDM. 75--84. Hanxiong Chen Yunqi Li Shaoyun Shi Shuchang Liu He Zhu and Yongfeng Zhang. 2022. Graph collaborative reasoning. In WSDM. 75--84.","DOI":"10.1145\/3488560.3498410"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Hanxiong Chen Shaoyun Shi Yunqi Li and Yongfeng Zhang. 2021. Neural Collaborative Reasoning. In WWW. 1516--1527. Hanxiong Chen Shaoyun Shi Yunqi Li and Yongfeng Zhang. 2021. Neural Collaborative Reasoning. In WWW. 1516--1527.","DOI":"10.1145\/3442381.3449973"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331196"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Chen Cheng Fen Xia Tong Zhang Irwin King and Michael R Lyu. 2014. Gradient boosting factorization machines. In RecSys. 265--272. Chen Cheng Fen Xia Tong Zhang Irwin King and Michael R Lyu. 2014. Gradient boosting factorization machines. In RecSys. 265--272.","DOI":"10.1145\/2645710.2645730"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371824"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In IJCAI. 1725--1731. Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In IJCAI. 1725--1731.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_1_12_1","volume-title":"Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political analysis 20, 1","author":"Hainmueller Jens","year":"2012","unstructured":"Jens Hainmueller . 2012. Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political analysis 20, 1 ( 2012 ), 25--46. Jens Hainmueller. 2012. Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political analysis 20, 1 (2012), 25--46."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080777"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Yuchin Juan Yong Zhuang Wei-Sheng Chin and Chih-Jen Lin. 2016. Field-aware factorization machines for CTR prediction. In RecSys. 43--50. Yuchin Juan Yong Zhuang Wei-Sheng Chin and Chih-Jen Lin. 2016. Field-aware factorization machines for CTR prediction. In RecSys. 43--50.","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Santosh Kabbur Xia Ning and George Karypis. 2013. Fism: factored item similarity models for top-n recommender systems. In SIGKDD. 659--667. Santosh Kabbur Xia Ning and George Karypis. 2013. Fism: factored item similarity models for top-n recommender systems. In SIGKDD. 659--667.","DOI":"10.1145\/2487575.2487589"},{"key":"e_1_3_2_1_17_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Kun Kuang Peng Cui Susan Athey Ruoxuan Xiong and Bo Li. 2018. Stable prediction across unknown environments. In SIGKDD. 1617--1626. Kun Kuang Peng Cui Susan Athey Ruoxuan Xiong and Bo Li. 2018. Stable prediction across unknown environments. In SIGKDD. 1617--1626.","DOI":"10.1145\/3219819.3220082"},{"key":"e_1_3_2_1_21_1","volume-title":"Building machines that learn and think like people. Behavioral and brain sciences 40","author":"Lake Brenden M","year":"2017","unstructured":"Brenden M Lake , Tomer D Ullman , Joshua B Tenenbaum , and Samuel J Gershman . 2017. Building machines that learn and think like people. Behavioral and brain sciences 40 ( 2017 ). Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. 2017. Building machines that learn and think like people. Behavioral and brain sciences 40 (2017)."},{"key":"e_1_3_2_1_22_1","volume-title":"Towards Personalized Fairness based on Causal Notion. SIGIR","author":"Li Yunqi","year":"2021","unstructured":"Yunqi Li , Hanxiong Chen , Shuyuan Xu , Yingqiang Ge , and Yongfeng Zhang . 2021. Towards Personalized Fairness based on Causal Notion. SIGIR ( 2021 ). Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2021. Towards Personalized Fairness based on Causal Notion. SIGIR (2021)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403314"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.83"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Dugang Liu Pengxiang Cheng Zhenhua Dong Xiuqiang He Weike Pan and Zhong Ming. 2020. A general knowledge distillation framework for counterfactual recommendation via uniform data. In SIGIR. 831--840. Dugang Liu Pengxiang Cheng Zhenhua Dong Xiuqiang He Weike Pan and Zhong Ming. 2020. A general knowledge distillation framework for counterfactual recommendation via uniform data. In SIGIR. 831--840.","DOI":"10.1145\/3397271.3401083"},{"key":"e_1_3_2_1_26_1","unstructured":"Haochen Liu Da Tang Ji Yang Xiangyu Zhao Jiliang Tang and Youlong Cheng. 2021. Self-supervised Learning for Alleviating Selection Bias in Recommendation Systems. (2021). Haochen Liu Da Tang Ji Yang Xiangyu Zhao Jiliang Tang and Youlong Cheng. 2021. Self-supervised Learning for Alleviating Selection Bias in Recommendation Systems. (2021)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401087"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"David Lopez-Paz Robert Nishihara Soumith Chintala Bernhard Scholkopf and L\u00e9on Bottou. 2017. Discovering causal signals in images. In CVPR. 6979--6987. David Lopez-Paz Robert Nishihara Soumith Chintala Bernhard Scholkopf and L\u00e9on Bottou. 2017. Discovering causal signals in images. In CVPR. 6979--6987.","DOI":"10.1109\/CVPR.2017.14"},{"key":"e_1_3_2_1_29_1","volume-title":"Feature selection for FM-based context-aware recommendation systems","author":"Mao Xueyu","unstructured":"Xueyu Mao , Saayan Mitra , and Viswanathan Swaminathan . 2017. Feature selection for FM-based context-aware recommendation systems . In ISM. IEEE , 252--255. Xueyu Mao, Saayan Mitra, and Viswanathan Swaminathan. 2017. Feature selection for FM-based context-aware recommendation systems. In ISM. IEEE, 252--255."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Rishabh Misra Mengting Wan and Julian McAuley. 2018. Decomposing fit semantics for product size recommendation in metric spaces. In RecSys. 422--426. Rishabh Misra Mengting Wan and Julian McAuley. 2018. Decomposing fit semantics for product size recommendation in metric spaces. In RecSys. 422--426.","DOI":"10.1145\/3240323.3240398"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Trung V Nguyen Alexandros Karatzoglou and Linas Baltrunas. 2014. Gaussian process factorization machines for context-aware recommendations. In SIGIR. 63--72. Trung V Nguyen Alexandros Karatzoglou and Linas Baltrunas. 2014. Gaussian process factorization machines for context-aware recommendations. In SIGIR. 63--72.","DOI":"10.1145\/2600428.2609623"},{"key":"e_1_3_2_1_32_1","volume-title":"Sparse factorization machines for click-through rate prediction","author":"Pan Zhen","unstructured":"Zhen Pan , Enhong Chen , Qi Liu , Tong Xu , Haiping Ma , and Hongjie Lin . 2016. Sparse factorization machines for click-through rate prediction . In ICDM. IEEE , 400--409. Zhen Pan, Enhong Chen, Qi Liu, Tong Xu, Haiping Ma, and Hongjie Lin. 2016. Sparse factorization machines for click-through rate prediction. In ICDM. IEEE, 400--409."},{"key":"e_1_3_2_1_33_1","article-title":"Using Markov blankets for causal structure learning","volume":"9","author":"Pellet Jean-Philippe","year":"2008","unstructured":"Jean-Philippe Pellet and Andr\u00e9 Elisseeff . 2008 . Using Markov blankets for causal structure learning . Journal of Machine Learning Research 9 , 7 (2008). Jean-Philippe Pellet and Andr\u00e9 Elisseeff. 2008. Using Markov blankets for causal structure learning. Journal of Machine Learning Research 9, 7 (2008).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186148"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2168752.2168771"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_3_2_1_38_1","unstructured":"Tobias Schnabel Adith Swaminathan Ashudeep Singh Navin Chandak and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In ICML. PMLR 1670--1679. Tobias Schnabel Adith Swaminathan Ashudeep Singh Navin Chandak and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In ICML. PMLR 1670--1679."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240577"},{"key":"e_1_3_2_1_40_1","volume-title":"Support vector machines","author":"Steinwart Ingo","unstructured":"Ingo Steinwart and Andreas Christmann . 2008. Support vector machines . Springer Science & Business Media . Ingo Steinwart and Andreas Christmann. 2008. Support vector machines. Springer Science & Business Media."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511948"},{"key":"e_1_3_2_1_42_1","volume-title":"Counterfactual explainable recommendation. CIKM","author":"Tan Juntao","year":"2021","unstructured":"Juntao Tan , Shuyuan Xu , Yingqiang Ge , Yunqi Li , Xu Chen , and Yongfeng Zhang . 2021. Counterfactual explainable recommendation. CIKM ( 2021 ). Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021. Counterfactual explainable recommendation. CIKM (2021)."},{"key":"e_1_3_2_1_43_1","volume-title":"Learning from missing data using selection bias in movie recommendation","author":"Vernade Claire","unstructured":"Claire Vernade and Olivier Capp\u00e9 . 2015. Learning from missing data using selection bias in movie recommendation . In DSAA. IEEE , 1--9. Claire Vernade and Olivier Capp\u00e9. 2015. Learning from missing data using selection bias in movie recommendation. In DSAA. IEEE, 1--9."},{"key":"e_1_3_2_1_44_1","volume-title":"Wayne Xin Zhao, and Ji-Rong Wen","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. 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."},{"key":"e_1_3_2_1_45_1","volume-title":"Are Neural Ranking Models Robust? arXiv preprint arXiv:2108.05018","author":"Wu Chen","year":"2021","unstructured":"Chen Wu , Ruqing Zhang , Jiafeng Guo , Yixing Fan , and Xueqi Cheng . 2021. Are Neural Ranking Models Robust? arXiv preprint arXiv:2108.05018 ( 2021 ). Chen Wu, Ruqing Zhang, Jiafeng Guo, Yixing Fan, and Xueqi Cheng. 2021. Are Neural Ranking Models Robust? arXiv preprint arXiv:2108.05018 (2021)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Jun Xiao Hao Ye Xiangnan He Hanwang Zhang Fei Wu and Tat-Seng Chua. 2017. Attentional factorization machines: learning the weight of feature interactions via attention networks. In IJCAI. 3119--3125. Jun Xiao Hao Ye Xiangnan He Hanwang Zhang Fei Wu and Tat-Seng Chua. 2017. Attentional factorization machines: learning the weight of feature interactions via attention networks. In IJCAI. 3119--3125.","DOI":"10.24963\/ijcai.2017\/435"},{"key":"e_1_3_2_1_47_1","volume-title":"Factorization machine based service recommendation on heterogeneous information networks","author":"Xie Fenfang","unstructured":"Fenfang Xie , Liang Chen , Yongjian Ye , Zibin Zheng , and Xiaola Lin . 2018. Factorization machine based service recommendation on heterogeneous information networks . In ICWS. IEEE , 115--122. Fenfang Xie, Liang Chen, Yongjian Ye, Zibin Zheng, and Xiaola Lin. 2018. Factorization machine based service recommendation on heterogeneous information networks. In ICWS. IEEE, 115--122."},{"key":"e_1_3_2_1_48_1","volume-title":"Binbin Hu, Zhiqiang Zhang, and Jun Zhou.","author":"Xiong Kun","year":"2021","unstructured":"Kun Xiong , Wenwen Ye , Xu Chen , Yongfeng Zhang , Wayne Xin Zhao , Binbin Hu, Zhiqiang Zhang, and Jun Zhou. 2021 . Counterfactual Review-based Recommendation. In CIKM. 2231--2240. Kun Xiong, Wenwen Ye, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao, Binbin Hu, Zhiqiang Zhang, and Jun Zhou. 2021. Counterfactual Review-based Recommendation. In CIKM. 2231--2240."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974348.13"},{"key":"e_1_3_2_1_50_1","volume-title":"Causal Collaborative Filtering. arXiv preprint arXiv:2102.01868","author":"Xu Shuyuan","year":"2021","unstructured":"Shuyuan Xu , Yingqiang Ge , Yunqi Li , Zuohui Fu , Xu Chen , and Yongfeng Zhang . 2021. Causal Collaborative Filtering. arXiv preprint arXiv:2102.01868 ( 2021 ). Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, and Yongfeng Zhang. 2021. Causal Collaborative Filtering. arXiv preprint arXiv:2102.01868 (2021)."},{"key":"e_1_3_2_1_51_1","volume-title":"The 1st International Workshop on Causality in Search and Recommendation.","author":"Xu Shuyuan","year":"2021","unstructured":"Shuyuan Xu , Yunqi Li , Shuchang Liu , Zuohui Fu , Yingqiang Ge , Xu Chen , and Yongfeng Zhang . 2021 . Learning causal explanations for recommendation . In The 1st International Workshop on Causality in Search and Recommendation. Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Xu Chen, and Yongfeng Zhang. 2021. Learning causal explanations for recommendation. In The 1st International Workshop on Causality in Search and Recommendation."},{"key":"e_1_3_2_1_52_1","volume-title":"Deconfounded Causal Collaborative Filtering. arXiv preprint arXiv:2110.07122","author":"Xu Shuyuan","year":"2021","unstructured":"Shuyuan Xu , Juntao Tan , Shelby Heinecke , Jia Li , and Yongfeng Zhang . 2021. Deconfounded Causal Collaborative Filtering. arXiv preprint arXiv:2110.07122 ( 2021 ). Shuyuan Xu, Juntao Tan, Shelby Heinecke, Jia Li, and Yongfeng Zhang. 2021. Deconfounded Causal Collaborative Filtering. arXiv preprint arXiv:2110.07122 (2021)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Makoto Yamada Wenzhao Lian Amit Goyal Jianhui Chen Kishan Wimalawarne Suleiman A Khan Samuel Kaski Hiroshi Mamitsuka and Yi Chang. 2017. Convex factorization machine for toxicogenomics prediction. In KDD. 1215--1224. Makoto Yamada Wenzhao Lian Amit Goyal Jianhui Chen Kishan Wimalawarne Suleiman A Khan Samuel Kaski Hiroshi Mamitsuka and Yi Chang. 2017. Convex factorization machine for toxicogenomics prediction. In KDD. 1215--1224.","DOI":"10.1145\/3097983.3098103"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3025171.3025211"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2921026"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Xingxuan Zhang Peng Cui Renzhe Xu Linjun Zhou Yue He and Zheyan Shen. 2021. Deep Stable Learning for Out-Of-Distribution Generalization. In CVPR. 5372--5382. Xingxuan Zhang Peng Cui Renzhe Xu Linjun Zhou Yue He and Zheyan Shen. 2021. Deep Stable Learning for Out-Of-Distribution Generalization. In CVPR. 5372--5382.","DOI":"10.1109\/CVPR46437.2021.00533"},{"key":"e_1_3_2_1_57_1","volume-title":"Causal Intervention for Leveraging Popularity Bias in Recommendation. arXiv preprint arXiv:2105.06067","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. arXiv preprint arXiv:2105.06067 ( 2021 ). Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. Causal Intervention for Leveraging Popularity Bias in Recommendation. arXiv preprint arXiv:2105.06067 (2021)."},{"key":"e_1_3_2_1_58_1","unstructured":"Wayne Xin Zhao Junhua Chen Pengfei Wang Qi Gu and Ji-Rong Wen. 2020. Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms. In CIKM. 2329--2332. Wayne Xin Zhao Junhua Chen Pengfei Wang Qi Gu and Ji-Rong Wen. 2020. Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms. In CIKM. 2329--2332."}],"event":{"name":"JCDL '22: The ACM\/IEEE Joint Conference on Digital Libraries in 2022","location":"Cologne Germany","acronym":"JCDL '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval","IEEE Technical Committee on Digital Libraries (TC DL)"]},"container-title":["Proceedings of the 22nd ACM\/IEEE Joint Conference on Digital Libraries"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3529372.3530921","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3529372.3530921","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:38Z","timestamp":1750188638000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3529372.3530921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":57,"alternative-id":["10.1145\/3529372.3530921","10.1145\/3529372"],"URL":"https:\/\/doi.org\/10.1145\/3529372.3530921","relation":{},"subject":[],"published":{"date-parts":[[2022,6,20]]},"assertion":[{"value":"2022-06-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}