{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T22:36:52Z","timestamp":1764542212055,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1910154, IIS-2007907, IIS-2046457"],"award-info":[{"award-number":["IIS-1910154, IIS-2007907, IIS-2046457"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557300","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:22:22Z","timestamp":1665883342000},"page":"2301-2310","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Dynamic Causal Collaborative Filtering"],"prefix":"10.1145","author":[{"given":"Shuyuan","family":"Xu","sequence":"first","affiliation":[{"name":"Rutgers University, New Brunswick, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juntao","family":"Tan","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuohui","family":"Fu","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianchao","family":"Ji","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shelby","family":"Heinecke","sequence":"additional","affiliation":[{"name":"Salesforce Research, Palo Alto, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Henry Jacob Wheeler-Mackta, and Jeremy R Campo","author":"Allen David Paul","year":"2017","unstructured":"David Paul Allen , Henry Jacob Wheeler-Mackta, and Jeremy R Campo . 2017 . The effects of music recommendation engines on the filter bubble phenomenon. Interactive Qualifying Projects ( 2017). David Paul Allen, Henry Jacob Wheeler-Mackta, and Jeremy R Campo. 2017. The effects of music recommendation engines on the filter bubble phenomenon. Interactive Qualifying Projects (2017)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Cigdem Aslay Antonis Matakos Esther Galbrun and Aristides Gionis. 2018. Maximizing the diversity of exposure in a social network. In ICDM. 863--868. Cigdem Aslay Antonis Matakos Esther Galbrun and Aristides Gionis. 2018. Maximizing the diversity of exposure in a social network. In ICDM. 863--868.","DOI":"10.1109\/ICDM.2018.00102"},{"key":"e_1_3_2_1_3_1","volume-title":"Science","volume":"348","author":"Bakshy Eytan","year":"2015","unstructured":"Eytan Bakshy , Solomon Messing , and Lada A Adamic . 2015 . Exposure to ideologically diverse news and opinion on Facebook . Science , Vol. 348 , 6239 (2015), 1130--1132. Eytan Bakshy, Solomon Messing, and Lada A Adamic. 2015. Exposure to ideologically diverse news and opinion on Facebook. Science, Vol. 348, 6239 (2015), 1130--1132."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/0022250X.2018.1517761"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240360"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Jaime Carbonell and Jade Goldstein. 1998. The use of MMR diversity-based reranking for reordering documents and producing summaries. In SIGIR. Jaime Carbonell and Jade Goldstein. 1998. The use of MMR diversity-based reranking for reordering documents and producing summaries. In SIGIR.","DOI":"10.1145\/290941.291025"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Allison JB Chaney Brandon M Stewart and Barbara E Engelhardt. 2018. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In RecSys. 224--232. Allison JB Chaney Brandon M Stewart and Barbara E Engelhardt. 2018. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In RecSys. 224--232.","DOI":"10.1145\/3240323.3240370"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449973"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Uthsav Chitra and Christopher Musco. 2020. Analyzing the impact of filter bubbles on social network polarization. In WSDM. 115--123. Uthsav Chitra and Christopher Musco. 2020. Analyzing the impact of filter bubbles on social network polarization. In WSDM. 115--123.","DOI":"10.1145\/3336191.3371825"},{"key":"e_1_3_2_1_10_1","first-page":"81","article-title":"Collaborative filtering recommender systems","volume":"4","author":"Ekstrand Michael D","year":"2011","unstructured":"Michael D Ekstrand , John T Riedl , Joseph A Konstan , 2011 . Collaborative filtering recommender systems . Foundations and Trends in HCI , Vol. 4 , 2 (2011), 81 -- 173 . Michael D Ekstrand, John T Riedl, Joseph A Konstan, et al. 2011. Collaborative filtering recommender systems. Foundations and Trends in HCI, Vol. 4, 2 (2011), 81--173.","journal-title":"Foundations and Trends in HCI"},{"key":"e_1_3_2_1_11_1","volume-title":"CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System. arXiv:2204.01266","author":"Gao Chongming","year":"2022","unstructured":"Chongming Gao , Wenqiang Lei , Jiawei Chen , Shiqi Wang , Xiangnan He , Shijun Li , Biao Li , Yuan Zhang , and Peng Jiang . 2022 . CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System. arXiv:2204.01266 (2022). Chongming Gao, Wenqiang Lei, Jiawei Chen, Shiqi Wang, Xiangnan He, Shijun Li, Biao Li, Yuan Zhang, and Peng Jiang. 2022. CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System. arXiv:2204.01266 (2022)."},{"key":"e_1_3_2_1_12_1","volume-title":"Aristides Gionis, and Michael Mathioudakis.","author":"Garimella Kiran","year":"2018","unstructured":"Kiran Garimella , Gianmarco De Francisci Morales , Aristides Gionis, and Michael Mathioudakis. 2018 . Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship. In WWW. 913--922. Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2018. Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship. In WWW. 913--922."},{"key":"e_1_3_2_1_13_1","volume-title":"A Survey on Trustworthy Recommender Systems. arXiv:2207.12515","author":"Ge Yingqiang","year":"2022","unstructured":"Yingqiang Ge , Shuchang Liu , Zuohui Fu , Juntao Tan , Zelong Li , Shuyuan Xu , Yunqi Li , Yikun Xian , and Yongfeng Zhang . 2022a. A Survey on Trustworthy Recommender Systems. arXiv:2207.12515 ( 2022 ). Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, and Yongfeng Zhang. 2022a. A Survey on Trustworthy Recommender Systems. arXiv:2207.12515 (2022)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Y. Ge J. Tan Y. Zhu Y. Xia J. Luo S. Liu Z. Fu S. Geng Z. Li and Y. Zhang. 2022b. Explainable Fairness in Recommendation. In SIGIR. Y. Ge J. Tan Y. Zhu Y. Xia J. Luo S. Liu Z. Fu S. Geng Z. Li and Y. Zhang. 2022b. Explainable Fairness in Recommendation. In SIGIR.","DOI":"10.1145\/3477495.3531973"},{"key":"e_1_3_2_1_15_1","unstructured":"Yingqiang Ge Shuya Zhao Honglu Zhou Changhua Pei Fei Sun Wenwu Ou and Yongfeng Zhang. 2020. Understanding echo chambers in e-commerce recommender systems. In SIGIR. 2261--2270. Yingqiang Ge Shuya Zhao Honglu Zhou Changhua Pei Fei Sun Wenwu Ou and Yongfeng Zhang. 2020. Understanding echo chambers in e-commerce recommender systems. In SIGIR. 2261--2270."},{"key":"e_1_3_2_1_16_1","volume-title":"Rishiraj Saha Roy, and Gerhard Weikum","author":"Ghazimatin Azin","year":"2020","unstructured":"Azin Ghazimatin , Oana Balalau , Rishiraj Saha Roy, and Gerhard Weikum . 2020 . PRINCE : Provider-side interpretability with counterfactual explanations in recommender systems. In WSDM. 196--204. Azin Ghazimatin, Oana Balalau, Rishiraj Saha Roy, and Gerhard Weikum. 2020. PRINCE: Provider-side interpretability with counterfactual explanations in recommender systems. In WSDM. 196--204."},{"volume-title":"Causal inference in statistics: A primer","author":"Glymour Madelyn","key":"e_1_3_2_1_17_1","unstructured":"Madelyn Glymour , Judea Pearl , and Nicholas P Jewell . 2016. Causal inference in statistics: A primer . John Wiley & Sons . Madelyn Glymour, Judea Pearl, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer. John Wiley & Sons."},{"key":"e_1_3_2_1_18_1","volume-title":"The movielens datasets: History and context. Acm transactions on interactive intelligent systems","author":"Maxwell Harper F","year":"2015","unstructured":"F Maxwell Harper and Joseph A Konstan . 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems , Vol. 5 , 4 ( 2015 ), 1--19. F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems, Vol. 5, 4 (2015), 1--19."},{"key":"e_1_3_2_1_19_1","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR. 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_20_1","volume-title":"Communicating with algorithms: A transfer entropy analysis of emotions-based escapes from online echo chambers. Communication Methods & Measures","author":"Hilbert Martin","year":"2018","unstructured":"Martin Hilbert , Saifuddin Ahmed , Jaeho Cho , Billy Liu , and Jonathan Luu . 2018. Communicating with algorithms: A transfer entropy analysis of emotions-based escapes from online echo chambers. Communication Methods & Measures ( 2018 ). Martin Hilbert, Saifuddin Ahmed, Jaeho Cho, Billy Liu, and Jonathan Luu. 2018. Communicating with algorithms: A transfer entropy analysis of emotions-based escapes from online echo chambers. Communication Methods & Measures (2018)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Ray Jiang Silvia Chiappa Tor Lattimore Andr\u00e1s Gy\u00f6rgy and Pushmeet Kohli. 2019. Degenerate feedback loops in recommender systems. In AIES. 383--390. Ray Jiang Silvia Chiappa Tor Lattimore Andr\u00e1s Gy\u00f6rgy and Pushmeet Kohli. 2019. Degenerate feedback loops in recommender systems. In AIES. 383--390.","DOI":"10.1145\/3306618.3314288"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Dimitris Kalimeris Smriti Bhagat Shankar Kalyanaraman and Udi Weinsberg. 2021. Preference Amplification in Recommender Systems. In KDD. 805--815. Dimitris Kalimeris Smriti Bhagat Shankar Kalyanaraman and Udi Weinsberg. 2021. Preference Amplification in Recommender Systems. In KDD. 805--815.","DOI":"10.1145\/3447548.3467298"},{"key":"e_1_3_2_1_23_1","volume-title":"serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems","author":"Kaminskas Marius","year":"2016","unstructured":"Marius Kaminskas and Derek Bridge . 2016. Diversity , serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems ( 2016 ). Marius Kaminskas and Derek Bridge. 2016. Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems (2016)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In KDD. 1748--1757. Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In KDD. 1748--1757.","DOI":"10.1145\/3394486.3403226"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/e19070371"},{"key":"e_1_3_2_1_27_1","volume-title":"Diversity in recommender systems--A survey. Knowledge-based systems","author":"Kunaver Matevvz","year":"2017","unstructured":"Matevvz Kunaver and Tomavz Povz rl. 2017. Diversity in recommender systems--A survey. Knowledge-based systems , Vol. 123 ( 2017 ), 154--162. Matevvz Kunaver and Tomavz Povz rl. 2017. Diversity in recommender systems--A survey. Knowledge-based systems, Vol. 123 (2017), 154--162."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Yunqi Li Hanxiong Chen Juntao Tan and Yongfeng Zhang. 2022a. Causal factorization machine for robust recommendation. In JCDL. 1--9. Yunqi Li Hanxiong Chen Juntao Tan and Yongfeng Zhang. 2022a. Causal factorization machine for robust recommendation. In JCDL. 1--9.","DOI":"10.1145\/3529372.3530921"},{"key":"e_1_3_2_1_29_1","volume-title":"Fairness in Recommendation: A Survey. arXiv: 2205.13619","author":"Li Yunqi","year":"2022","unstructured":"Yunqi Li , Hanxiong Chen , Shuyuan Xu , Yingqiang Ge , Juntao Tan , Shuchang Liu , and Yongfeng Zhang . 2022b. Fairness in Recommendation: A Survey. arXiv: 2205.13619 ( 2022 ). Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2022b. Fairness in Recommendation: A Survey. arXiv: 2205.13619 (2022)."},{"key":"e_1_3_2_1_30_1","unstructured":"Yunqi Li Hanxiong Chen Shuyuan Xu Yingqiang Ge and Yongfeng Zhang. 2021. Towards Personalized Fairness Based on Causal Notion. In SIGIR. Yunqi Li Hanxiong Chen Shuyuan Xu Yingqiang Ge and Yongfeng Zhang. 2021. Towards Personalized Fairness Based on Causal Notion. In SIGIR."},{"key":"e_1_3_2_1_31_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_32_1","doi-asserted-by":"crossref","unstructured":"Antonis Matakos and Aristides Gionis. 2018. Tell me Something My Friends do not Know: Diversity Maximization in Social Networks. In ICDM. Antonis Matakos and Aristides Gionis. 2018. Tell me Something My Friends do not Know: Diversity Maximization in Social Networks. In ICDM.","DOI":"10.1109\/ICDM.2018.00048"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2018.1444076"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Tien T Nguyen Pik-Mai Hui F Maxwell Harper Loren Terveen and Joseph A Konstan. 2014. Exploring the filter bubble: the effect of using recommender systems on content diversity. In WWW. 677--686. Tien T Nguyen Pik-Mai Hui F Maxwell Harper Loren Terveen and Joseph A Konstan. 2014. Exploring the filter bubble: the effect of using recommender systems on content diversity. In WWW. 677--686.","DOI":"10.1145\/2566486.2568012"},{"key":"e_1_3_2_1_35_1","unstructured":"Jianmo Ni Jiacheng Li and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In EMNLP-IJCNLP. Jianmo Ni Jiacheng Li and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In EMNLP-IJCNLP."},{"key":"e_1_3_2_1_36_1","volume-title":"Modelling opinion dynamics in the age of algorithmic personalisation. Scientific reports","author":"Perra Nicola","year":"2019","unstructured":"Nicola Perra and Luis EC Rocha . 2019. Modelling opinion dynamics in the age of algorithmic personalisation. Scientific reports , Vol. 9 , 1 ( 2019 ), 1--11. Nicola Perra and Luis EC Rocha. 2019. Modelling opinion dynamics in the age of algorithmic personalisation. Scientific reports, Vol. 9, 1 (2019), 1--11."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Qiao Liu Yifu Zeng Refuoe Mokhosi Haibin Zhang. 2018. STAMP: short-term attention\/memory priority model for session-based recommendation. In KDD. Qiao Liu Yifu Zeng Refuoe Mokhosi Haibin Zhang. 2018. STAMP: short-term attention\/memory priority model for session-based recommendation. In KDD.","DOI":"10.1145\/3219819.3219950"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Zhen Qin Suming J Chen Donald Metzler Yongwoo Noh Jingzheng Qin and Xuanhui Wang. 2020. Attribute-based propensity for unbiased learning in recommender systems: Algorithm and case studies. In KDD. 2359--2367. Zhen Qin Suming J Chen Donald Metzler Yongwoo Noh Jingzheng Qin and Xuanhui Wang. 2020. Attribute-based propensity for unbiased learning in recommender systems: Algorithm and case studies. In KDD. 2359--2367.","DOI":"10.1145\/3394486.3403285"},{"key":"e_1_3_2_1_39_1","unstructured":"Marten Risius Okan Aydinguel and Maximilian Haug. 2019. Towards an understanding of conspiracy echo chambers on Facebook. AISeL. Marten Risius Okan Aydinguel and Maximilian Haug. 2019. Towards an understanding of conspiracy echo chambers on Facebook. AISeL."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Yuta Saito Suguru Yaginuma Yuta Nishino Hayato Sakata and Kazuhide Nakata. 2020. Unbiased recommender learning from missing-not-at-random implicit feedback. In WSDM. 501--509. Yuta Saito Suguru Yaginuma Yuta Nishino Hayato Sakata and Kazuhide Nakata. 2020. Unbiased recommender learning from missing-not-at-random implicit feedback. In WSDM. 501--509.","DOI":"10.1145\/3336191.3371783"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2019.00155"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Masahiro Sato Janmajay Singh Sho Takemori Takashi Sonoda Qian Zhang and Tomoko Ohkuma. 2019b. Uplift-based evaluation and optimization of recommenders. In RecSys. 296--304. Masahiro Sato Janmajay Singh Sho Takemori Takashi Sonoda Qian Zhang and Tomoko Ohkuma. 2019b. Uplift-based evaluation and optimization of recommenders. In RecSys. 296--304.","DOI":"10.1145\/3298689.3347018"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Masahiro Sato Sho Takemori Janmajay Singh and Tomoko Ohkuma. 2020. Unbiased learning for the causal effect of recommendation. In RecSys. 378--387. Masahiro Sato Sho Takemori Janmajay Singh and Tomoko Ohkuma. 2020. Unbiased learning for the causal effect of recommendation. In RecSys. 378--387.","DOI":"10.1145\/3383313.3412261"},{"key":"e_1_3_2_1_44_1","unstructured":"T. Schnabel A. Swaminathan A. Singh N. Chandak and T. Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In ICML. T. Schnabel A. Swaminathan A. Singh N. Chandak and T. Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In ICML."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3317589"},{"volume-title":"Going to extremes: How like minds unite and divide","author":"Sunstein Cass R","key":"e_1_3_2_1_46_1","unstructured":"Cass R Sunstein . 2009. Going to extremes: How like minds unite and divide . Oxford University Press . Cass R Sunstein. 2009. Going to extremes: How like minds unite and divide. Oxford University Press."},{"key":"e_1_3_2_1_47_1","unstructured":"Juntao Tan Shijie Geng Zuohui Fu Yingqiang Ge Shuyuan Xu Yunqi Li and Yongfeng Zhang. 2022. Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning. In WWW. 1018--1027. Juntao Tan Shijie Geng Zuohui Fu Yingqiang Ge Shuyuan Xu Yunqi Li and Yongfeng Zhang. 2022. Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning. In WWW. 1018--1027."},{"key":"e_1_3_2_1_48_1","unstructured":"Juntao Tan Shuyuan Xu Yingqiang Ge Yunqi Li Xu Chen and Yongfeng Zhang. 2021. Counterfactual explainable recommendation. In CIKM. Juntao Tan Shuyuan Xu Yingqiang Ge Yunqi Li Xu Chen and Yongfeng Zhang. 2021. Counterfactual explainable recommendation. In CIKM."},{"volume-title":"An introduction to Markov chains. Department of Mathematical Sciences","author":"Tolver Anders","key":"e_1_3_2_1_49_1","unstructured":"Anders Tolver . 2016. An introduction to Markov chains. Department of Mathematical Sciences , University of Copenhagen (2016) . Anders Tolver. 2016. An introduction to Markov chains. Department of Mathematical Sciences, University of Copenhagen (2016)."},{"key":"e_1_3_2_1_50_1","volume-title":"Counterfactual Explanations for Neural Recommenders. In SIGIR (SIGIR '21)","author":"Tran Khanh Hiep","year":"2021","unstructured":"Khanh Hiep Tran , Azin Ghazimatin , and Rishiraj Saha Roy . 2021 . Counterfactual Explanations for Neural Recommenders. In SIGIR (SIGIR '21) . 1627--1631. Khanh Hiep Tran, Azin Ghazimatin, and Rishiraj Saha Roy. 2021. Counterfactual Explanations for Neural Recommenders. In SIGIR (SIGIR '21). 1627--1631."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Yixin Wang Dawen Liang Laurent Charlin and David M Blei. 2020. Causal inference for recommender systems. In RecSys. 426--431. Yixin Wang Dawen Liang Laurent Charlin and David M Blei. 2020. Causal inference for recommender systems. In RecSys. 426--431.","DOI":"10.1145\/3383313.3412225"},{"key":"e_1_3_2_1_52_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_53_1","doi-asserted-by":"crossref","unstructured":"Tianxin Wei Fuli Feng Jiawei Chen Ziwei Wu Jinfeng Yi and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In KDD. 1791--1800. Tianxin Wei Fuli Feng Jiawei Chen Ziwei Wu Jinfeng Yi and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In KDD. 1791--1800.","DOI":"10.1145\/3447548.3467289"},{"key":"e_1_3_2_1_54_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. 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."},{"key":"e_1_3_2_1_55_1","volume-title":"Causal Collaborative Filtering. arXiv:2102.01868","author":"Xu Shuyuan","year":"2021","unstructured":"Shuyuan Xu , Yingqiang Ge , Yunqi Li , Zuohui Fu , Xu Chen , and Yongfeng Zhang . 2021a. Causal Collaborative Filtering. arXiv:2102.01868 ( 2021 ). Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, and Yongfeng Zhang. 2021a. Causal Collaborative Filtering. arXiv:2102.01868 (2021)."},{"key":"e_1_3_2_1_56_1","volume-title":"Deconfounded Causal Collaborative Filtering. arXiv:2110.07122","author":"Xu Shuyuan","year":"2021","unstructured":"Shuyuan Xu , Juntao Tan , Shelby Heinecke , Jia Li , and Yongfeng Zhang . 2021b. Deconfounded Causal Collaborative Filtering. arXiv:2110.07122 ( 2021 ). Shuyuan Xu, Juntao Tan, Shelby Heinecke, Jia Li, and Yongfeng Zhang. 2021b. Deconfounded Causal Collaborative Filtering. arXiv:2110.07122 (2021)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"crossref","unstructured":"Longqi Yang Yin Cui Yuan Xuan Chenyang Wang Serge Belongie and Deborah Estrin. 2018. Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. In RecSys. 279--287. Longqi Yang Yin Cui Yuan Xuan Chenyang Wang Serge Belongie and Deborah Estrin. 2018. Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. In RecSys. 279--287.","DOI":"10.1145\/3240323.3240355"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"crossref","unstructured":"Mengyue Yang Quanyu Dai Zhenhua Dong Xu Chen Xiuqiang He and Jun Wang. 2021. Top-N Recommendation with Counterfactual User Preference Simulation. In CIKM. Mengyue Yang Quanyu Dai Zhenhua Dong Xu Chen Xiuqiang He and Jun Wang. 2021. Top-N Recommendation with Counterfactual User Preference Simulation. In CIKM.","DOI":"10.1145\/3459637.3482305"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"crossref","unstructured":"Junzhe Zhang and Elias Bareinboim. 2018. Fairness in decision-making-the causal explanation formula. In AAAI. Junzhe Zhang and Elias Bareinboim. 2018. Fairness in decision-making-the causal explanation formula. In AAAI.","DOI":"10.1609\/aaai.v32i1.11564"},{"key":"e_1_3_2_1_60_1","volume-title":"Causerec: Counterfactual user sequence synthesis for sequential recommendation. In SIGIR. 367--377.","author":"Zhang Shengyu","year":"2021","unstructured":"Shengyu Zhang , Dong Yao , Zhou Zhao , Tat-Seng Chua , and Fei Wu . 2021 . Causerec: Counterfactual user sequence synthesis for sequential recommendation. In SIGIR. 367--377. Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, and Fei Wu. 2021. Causerec: Counterfactual user sequence synthesis for sequential recommendation. In SIGIR. 367--377."},{"key":"e_1_3_2_1_61_1","volume-title":"Deep learning based recommender system: A survey and new perspectives. Comput. Surveys","author":"Zhang Shuai","year":"2019","unstructured":"Shuai Zhang , Lina Yao , Aixin Sun , and Yi Tay . 2019. Deep learning based recommender system: A survey and new perspectives. Comput. Surveys ( 2019 ). Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. Comput. Surveys (2019)."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1561\/1500000066"},{"key":"e_1_3_2_1_63_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."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449788"}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Atlanta GA USA","acronym":"CIKM '22"},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557300","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557300","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557300","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:28Z","timestamp":1750182568000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":64,"alternative-id":["10.1145\/3511808.3557300","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557300","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}