{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:10:13Z","timestamp":1755839413014,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":54,"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":[{"name":"Army Research Office (ARO)","award":["W911NF-21-1-0198"],"award-info":[{"award-number":["W911NF-21-1-0198"]}]},{"name":"National Science Foundation (NSF)","award":["IIS-1909702 and IIS1955851"],"award-info":[{"award-number":["IIS-1909702 and IIS1955851"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557431","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:22:22Z","timestamp":1665883342000},"page":"2220-2229","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Representation Matters When Learning From Biased Feedback in Recommendation"],"prefix":"10.1145","author":[{"given":"Teng","family":"Xiao","sequence":"first","affiliation":[{"name":"The Pennsylvania State University, State College, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, UNK, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suhang","family":"Wang","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, State College, PA, 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":"Francc ois Laviolette, and Mario Marchand","author":"Ajakan Hana","year":"2014","unstructured":"Hana Ajakan , Pascal Germain , Hugo Larochelle , Francc ois Laviolette, and Mario Marchand . 2014 . Domain-adversarial neural networks. In arXiv. Hana Ajakan, Pascal Germain, Hugo Larochelle, Francc ois Laviolette, and Mario Marchand. 2014. Domain-adversarial neural networks. In arXiv."},{"key":"e_1_3_2_1_2_1","volume-title":"Conference on Neural Information Processing Systems.","author":"Aneja Jyoti","year":"2021","unstructured":"Jyoti Aneja , Alex Schwing , Jan Kautz , and Arash Vahdat . 2021 . A contrastive learning approach for training variational autoencoder priors . In Conference on Neural Information Processing Systems. Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat. 2021. A contrastive learning approach for training variational autoencoder priors. In Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273507"},{"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","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462919"},{"key":"e_1_3_2_1_6_1","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. In arXiv.  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. In arXiv."},{"key":"e_1_3_2_1_7_1","volume-title":"Deep Tensor Factorization for Multi-Criteria Recommender Systems. In International Conference on Big Data (Big Data). 1046--1051","author":"Chen Zhengyu","year":"2019","unstructured":"Zhengyu Chen , Sibo Gai , and Donglin Wang . 2019 . Deep Tensor Factorization for Multi-Criteria Recommender Systems. In International Conference on Big Data (Big Data). 1046--1051 . Zhengyu Chen, Sibo Gai, and Donglin Wang. 2019. Deep Tensor Factorization for Multi-Criteria Recommender Systems. In International Conference on Big Data (Big Data). 1046--1051."},{"key":"e_1_3_2_1_8_1","volume-title":"Multi-Initialization Meta-Learning with Domain Adaptation. In The International Conference on Acoustics, Speech, & Signal Processing. 1390--1394","author":"Chen Zhengyu","year":"2021","unstructured":"Zhengyu Chen and Donglin Wang . 2021 . Multi-Initialization Meta-Learning with Domain Adaptation. In The International Conference on Acoustics, Speech, & Signal Processing. 1390--1394 . Zhengyu Chen and Donglin Wang. 2021. Multi-Initialization Meta-Learning with Domain Adaptation. In The International Conference on Acoustics, Speech, & Signal Processing. 1390--1394."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16521"},{"key":"e_1_3_2_1_10_1","volume-title":"Learning Bounds for Importance Weighting. In Conference on Neural Information Processing Systems.","author":"Cortes Corinna","year":"2010","unstructured":"Corinna Cortes , Yishay Mansour , and Mehryar Mohri . 2010 . Learning Bounds for Importance Weighting. In Conference on Neural Information Processing Systems. Corinna Cortes, Yishay Mansour, and Mehryar Mohri. 2010. Learning Bounds for Importance Weighting. In Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_11_1","unstructured":"Imre Csisz\u00e1r. 1967. Information-type measures of difference of probability distributions and indirect observation. In Studia Sci. Math. Hungar.  Imre Csisz\u00e1r. 1967. Information-type measures of difference of probability distributions and indirect observation. In Studia Sci. Math. Hungar."},{"key":"e_1_3_2_1_12_1","volume-title":"International Conference on Machine Learning.","author":"Dud'ik Miroslav","year":"2011","unstructured":"Miroslav Dud'ik , John Langford , and Lihong Li . 2011 . Doubly robust policy evaluation and learning . In International Conference on Machine Learning. Miroslav Dud'ik, John Langford, and Lihong Li. 2011. Doubly robust policy evaluation and learning. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_13_1","volume":"201","author":"Edwards Harrison","unstructured":"Harrison Edwards and Amos J. Storkey. 201 6. Censoring Representations with an Adversary. In International Conference on Learning Representations. Harrison Edwards and Amos J. Storkey. 2016. Censoring Representations with an Adversary. In International Conference on Learning Representations.","journal-title":"Amos J. Storkey."},{"key":"e_1_3_2_1_14_1","volume-title":"Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. In International Conference on Learning Representations.","author":"Eysenbach Benjamin","year":"2020","unstructured":"Benjamin Eysenbach , Shreyas Chaudhari , Swapnil Asawa , Sergey Levine , and Ruslan Salakhutdinov . 2020 . Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. In International Conference on Learning Representations. Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, and Ruslan Salakhutdinov. 2020. Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_15_1","volume-title":"Mario Marchand, and Victor Lempitsky.","author":"Ganin Yaroslav","year":"2016","unstructured":"Yaroslav Ganin , Evgeniya Ustinova , Hana Ajakan , Pascal Germain , Hugo Larochelle , Francc ois Laviolette , Mario Marchand, and Victor Lempitsky. 2016 . Domain-adversarial training of neural networks. In The Journal of Machine Learning Research . Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francc ois Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. In The Journal of Machine Learning Research."},{"key":"e_1_3_2_1_16_1","volume-title":"Generative Adversarial Nets. In Conference on Neural Information Processing System.","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014 . Generative Adversarial Nets. In Conference on Neural Information Processing System. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Conference on Neural Information Processing System."},{"key":"e_1_3_2_1_17_1","volume-title":"CounterFactual Regression with Importance Sampling Weights. In International Joint Conference on Artificial Intelligence.","author":"Hassanpour Negar","year":"2019","unstructured":"Negar Hassanpour and Russell Greiner . 2019 . CounterFactual Regression with Importance Sampling Weights. In International Joint Conference on Artificial Intelligence. Negar Hassanpour and Russell Greiner. 2019. CounterFactual Regression with Importance Sampling Weights. In International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_18_1","volume-title":"The World Wide Web Conference.","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 The World Wide Web Conference. Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In The World Wide Web Conference."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Kurt Hornik Maxwell Stinchcombe and Halbert White. 1989. Multilayer feedforward networks are universal approximators. In Neural networks.  Kurt Hornik Maxwell Stinchcombe and Halbert White. 1989. Multilayer feedforward networks are universal approximators. In Neural networks.","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Machine Learning.","author":"Johansson Fredrik","year":"2016","unstructured":"Fredrik Johansson , Uri Shalit , and David Sontag . 2016 . Learning representations for counterfactual inference . In International Conference on Machine Learning. Fredrik Johansson, Uri Shalit, and David Sontag. 2016. Learning representations for counterfactual inference. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_21_1","unstructured":"Fredrik D Johansson Nathan Kallus Uri Shalit and David Sontag. 2018. Learning weighted representations for generalization across designs. In arXiv.  Fredrik D Johansson Nathan Kallus Uri Shalit and David Sontag. 2018. Learning weighted representations for generalization across designs. In arXiv."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Yehuda Koren Robert Bell and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. In Computer.  Yehuda Koren Robert Bell and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. In Computer.","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_1_23_1","volume-title":"Transfer Learning in Collaborative Recommendation for Bias Reduction. In ACM Recommender Systems Conference.","author":"Lin Zinan","year":"2021","unstructured":"Zinan Lin , Dugang Liu , Weike Pan , and Zhong Ming . 2021 . Transfer Learning in Collaborative Recommendation for Bias Reduction. In ACM Recommender Systems Conference. Zinan Lin, Dugang Liu, Weike Pan, and Zhong Ming. 2021. Transfer Learning in Collaborative Recommendation for Bias Reduction. In ACM Recommender Systems Conference."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401083"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3474263"},{"key":"e_1_3_2_1_26_1","volume-title":"International Conference on Machine Learning.","author":"Madras David","year":"2018","unstructured":"David Madras , Elliot Creager , Toniann Pitassi , and Richard Zemel . 2018 . Learning adversarially fair and transferable representations . In International Conference on Machine Learning. David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018. Learning adversarially fair and transferable representations. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference on Learning Representations.","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry , Aleksandar Makelov , Ludwig Schmidt , Dimitris Tsipras , and Adrian Vladu . 2018 . Towards Deep Learning Models Resistant to Adversarial Attacks . In International Conference on Learning Representations. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_28_1","unstructured":"Benjamin M Marlin Richard S Zemel Sam Roweis and Malcolm Slaney. 2007. Collaborative filtering and the missing at random assumption. In UAI.  Benjamin M Marlin Richard S Zemel Sam Roweis and Malcolm Slaney. 2007. Collaborative filtering and the missing at random assumption. In UAI."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1018"},{"key":"e_1_3_2_1_30_1","volume-title":"Conference on Neural Information Processing System.","author":"Nowozin Sebastian","year":"2016","unstructured":"Sebastian Nowozin , Botond Cseke , and Ryota Tomioka . 2016 . f-gan: Training generative neural samplers using variational divergence minimization . In Conference on Neural Information Processing System. Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. f-gan: Training generative neural samplers using variational divergence minimization. In Conference on Neural Information Processing System."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412488"},{"volume-title":"Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability","author":"Alfr\u00e9d","key":"e_1_3_2_1_32_1","unstructured":"Alfr\u00e9d R\u00e9nyi et al. 1961. On measures of entropy and information . In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability , Volume 1: Contributions to the Theory of Statistics. Alfr\u00e9d R\u00e9nyi et al. 1961. On measures of entropy and information. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Noveen Sachdeva Yi Su and Thorsten Joachims. 2020. Off-policy bandits with deficient support. In KDD.  Noveen Sachdeva Yi Su and Thorsten Joachims. 2020. Off-policy bandits with deficient support. In KDD.","DOI":"10.1145\/3394486.3403139"},{"key":"e_1_3_2_1_34_1","volume-title":"Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. In ACM SIGIR Conference on Research and Development in Information Retrieval.","author":"Saito Yuta","year":"2020","unstructured":"Yuta Saito . 2020 a. Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. In ACM SIGIR Conference on Research and Development in Information Retrieval. Yuta Saito. 2020a. Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. In ACM SIGIR Conference on Research and Development in Information Retrieval."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412262"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371783"},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference on Machine Learning.","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 International Conference on Machine Learning. Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Machine Learning.","author":"Shalit Uri","year":"2017","unstructured":"Uri Shalit , Fredrik D Johansson , and David Sontag . 2017 . Estimating individual treatment effect: generalization bounds and algorithms . In International Conference on Machine Learning. Uri Shalit, Fredrik D Johansson, and David Sontag. 2017. Estimating individual treatment effect: generalization bounds and algorithms. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_39_1","volume-title":"Conference on Neural Information Processing System.","author":"Sun Xinwei","year":"2021","unstructured":"Xinwei Sun , Botong Wu , Xiangyu Zheng , Chang Liu , Wei Chen , Tao Qin , and Tie-Yan Liu . 2021 . Recovering Latent Causal Factor for Generalization to Distributional Shifts . In Conference on Neural Information Processing System. Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, and Tie-Yan Liu. 2021. Recovering Latent Causal Factor for Generalization to Distributional Shifts. In Conference on Neural Information Processing System."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Adith Swaminathan and Thorsten Joachims. 2015a. Batch learning from logged bandit feedback through counterfactual risk minimization. In The Journal of Machine Learning Research.  Adith Swaminathan and Thorsten Joachims. 2015a. Batch learning from logged bandit feedback through counterfactual risk minimization. In The Journal of Machine Learning Research.","DOI":"10.1145\/2740908.2742564"},{"key":"e_1_3_2_1_41_1","volume-title":"Conference on Neural Information Processing System.","author":"Swaminathan Adith","year":"2015","unstructured":"Adith Swaminathan and Thorsten Joachims . 2015 b. The self-normalized estimator for counterfactual learning . In Conference on Neural Information Processing System. Adith Swaminathan and Thorsten Joachims. 2015b. The self-normalized estimator for counterfactual learning. In Conference on Neural Information Processing System."},{"key":"e_1_3_2_1_42_1","volume-title":"International Conference on Machine Learning.","author":"Teshima Takeshi","year":"2020","unstructured":"Takeshi Teshima , Issei Sato , and Masashi Sugiyama . 2020 . Few-shot domain adaptation by causal mechanism transfer . In International Conference on Machine Learning. Takeshi Teshima, Issei Sato, and Masashi Sugiyama. 2020. Few-shot domain adaptation by causal mechanism transfer. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_43_1","volume-title":"International Conference on Machine Learning.","author":"Wang Xiaojie","year":"2019","unstructured":"Xiaojie Wang , Rui Zhang , Yu Sun , and Jianzhong Qi . 2019 . Doubly robust joint learning for recommendation on data missing not at random . In International Conference on Machine Learning. Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi. 2019. Doubly robust joint learning for recommendation on data missing not at random. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441799"},{"key":"e_1_3_2_1_45_1","volume-title":"Conference on Neural Information Processing System.","author":"Wang Zifeng","year":"2020","unstructured":"Zifeng Wang , Xi Chen , Rui Wen , Shao-Lun Huang , Ercan Kuruoglu , and Yefeng Zheng . 2020 . Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback . In Conference on Neural Information Processing System. Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu, and Yefeng Zheng. 2020. Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback. In Conference on Neural Information Processing System."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16579"},{"key":"e_1_3_2_1_47_1","volume-title":"Towards Off-Policy Learning for Ranking Policies with Logged Feedback. In AAAI Conference on Artificial Intelligence. 8700--8707","author":"Xiao Teng","year":"2022","unstructured":"Teng Xiao and Suhang Wang . 2022 a. Towards Off-Policy Learning for Ranking Policies with Logged Feedback. In AAAI Conference on Artificial Intelligence. 8700--8707 . Teng Xiao and Suhang Wang. 2022a. Towards Off-Policy Learning for Ranking Policies with Logged Feedback. In AAAI Conference on Artificial Intelligence. 8700--8707."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498521"},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Learning Representations.","author":"Xie Yuan","year":"2018","unstructured":"Yuan Xie , Boyi Liu , Qiang Liu , Zhaoran Wang , Yuan Zhou , and Jian Peng . 2018 . Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy . In International Conference on Learning Representations. Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, and Jian Peng. 2018. Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_50_1","volume-title":"Adversarial Counterfactual Learning and Evaluation for Recommender System. In Conference on Neural Information Processing System.","author":"Xu Da","year":"2020","unstructured":"Da Xu , Chuanwei Ruan , Evren Korpeoglu , Sushant Kumar , and Kannan Achan . 2020 a. Adversarial Counterfactual Learning and Evaluation for Recommender System. In Conference on Neural Information Processing System. Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020a. Adversarial Counterfactual Learning and Evaluation for Recommender System. In Conference on Neural Information Processing System."},{"key":"e_1_3_2_1_51_1","volume-title":"International Conference on Learning Representations.","author":"Xu Da","year":"2020","unstructured":"Da Xu , Yuting Ye , and Chuanwei Ruan . 2020 b. Understanding the role of importance weighting for deep learning . In International Conference on Learning Representations. Da Xu, Yuting Ye, and Chuanwei Ruan. 2020b. Understanding the role of importance weighting for deep learning. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_52_1","volume-title":"International Conference on Learning Representations.","author":"Yoon Jinsung","year":"2018","unstructured":"Jinsung Yoon , James Jordon , and Mihaela Van Der Schaar . 2018 . GANITE: Estimation of individualized treatment effects using generative adversarial nets . In International Conference on Learning Representations. Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar. 2018. GANITE: Estimation of individualized treatment effects using generative adversarial nets. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_53_1","volume-title":"Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In The Web Conference.","author":"Zheng Yu","year":"2021","unstructured":"Yu Zheng , Chen Gao , Xiang Li , Xiangnan He , Yong Li , and Depeng Jin . 2021 . Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In The Web Conference. Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2021. Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In The Web Conference."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412210"}],"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\/3511808.3557431","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:55Z","timestamp":1750182535000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":54,"alternative-id":["10.1145\/3511808.3557431","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557431","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"}}]}}