{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T21:15:45Z","timestamp":1773868545700,"version":"3.50.1"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"7","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"U.S. Navy Office of Naval Research"},{"DOI":"10.13039\/100006754","name":"U.S. Army Research Laboratory","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"crossref"}]},{"name":"U.S. USACE","award":["GR40695"],"award-info":[{"award-number":["GR40695"]}]},{"name":"Designing nature to enhance resilience of built infrastructure","award":["NSF#1909555"],"award-info":[{"award-number":["NSF#1909555"]}]},{"name":"pCAR: Discovering and Leveraging Plausibly Causal (p-causal) Relationships to Understand Complex Dynamic Systems","award":["NSF 2200140"],"award-info":[{"award-number":["NSF 2200140"]}]},{"name":"PIPP Phase I: Predicting Emergence in Multidisciplinary Pandemic Tipping-points","award":["NSF 2230748"],"award-info":[{"award-number":["NSF 2230748"]}]},{"name":"PIRE: Building Decarbonization via AI-empowered District Heat Pump Systems","award":["NSF 2125246"],"award-info":[{"award-number":["NSF 2125246"]}]},{"name":"SCC-IRG JST: PanCommunity: Leveraging Data and Models for Understanding and Improving Community Response in Pandemic","award":["ARO W911NF2110030, ARL W911NF2020124, ONR N00014-21-1-4002"],"award-info":[{"award-number":["ARO W911NF2110030, ARL W911NF2020124, ONR N00014-21-1-4002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            Online user engagement is highly influenced by various machine learning models, such as recommender systems. These systems recommend new items to the user based on the user\u2019s historical interactions. Implicit recommender systems reflect a binary setting showing whether a user interacted (e.g., clicked on) with an item or not. However, the observed clicks may be due to various causes such as user\u2019s interest, item\u2019s popularity, and social influence factors. Traditional recommender systems consider these causes under a unified representation, which may lead to the emergence and amplification of various biases in recommendations. However, recent work indicates that by disentangling the unified representations, one can mitigate bias (e.g., popularity bias) in recommender systems and help improve recommendation performance. Yet, prior work in causal disentanglement in recommendations does not consider a crucial factor, that is, social influence. Social theories such as homophily and social influence provide evidence that a user\u2019s decision can be highly influenced by the user\u2019s social relations. Thus, accounting for the social relations while disentangling leads to less biased recommendations. To this end, we identify three separate causes behind an effect (e.g., clicks): (a) user\u2019s interest, (b) item\u2019s popularity, and (c) user\u2019s social influence. Our approach seeks to causally disentangle the user and item latent features to mitigate popularity bias in\n            <jats:italic>implicit<\/jats:italic>\n            feedback\u2013based social recommender systems. To achieve this goal, we draw from causal inference theories and social network theories and propose a causality-aware disentanglement method that leverages both the user\u2013item interaction network and auxiliary social network information. Experiments on real-world datasets against various state-of-the-art baselines validate the effectiveness of the proposed model for mitigating popularity bias and generating de-biased recommendations.\n          <\/jats:p>","DOI":"10.1145\/3582435","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T12:25:30Z","timestamp":1675254330000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Causal Disentanglement for Implicit Recommendations with Network Information"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6186-6946","authenticated-orcid":false,"given":"Paras","family":"Sheth","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Arizona State University, Tempe, AZ, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8522-6142","authenticated-orcid":false,"given":"Ruocheng","family":"Guo","sequence":"additional","affiliation":[{"name":"Bytedance, Barbican, London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2503-2522","authenticated-orcid":false,"given":"Lu","family":"Cheng","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Arizona State University, Tempe, AZ, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3264-7904","authenticated-orcid":false,"given":"Huan","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Arizona State University, Tempe, AZ, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4977-6646","authenticated-orcid":false,"given":"Kasim Sel\u00e7uk","family":"Candan","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Arizona State University, Tempe, AZ, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"RecSys","author":"Abdollahpouri Himan","year":"2017","unstructured":"Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling popularity bias in learning-to-rank recommendation. In RecSys."},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Stephen Bonner and Flavian Vasile. 2018. Causal embeddings for recommendation. Proceedings of the 12th ACM Conference on Recommender Systems Association for Computing Machinery New York NY 104\u2013112.","DOI":"10.1145\/3240323.3240360"},{"key":"e_1_3_2_4_2","volume-title":"SIGIR","author":"Bruch Sebastian","year":"2019","unstructured":"Sebastian Bruch, Xuanhui Wang, Michael Bendersky, and Marc Najork. 2019. An analysis of the softmax cross entropy loss for learning-to-rank with binary relevance. In SIGIR."},{"key":"e_1_3_2_5_2","volume-title":"RSWeb@ RecSys","author":"Ca\u00f1amares Roc\u00edo","year":"2014","unstructured":"Roc\u00edo Ca\u00f1amares and Pablo Castells. 2014. Exploring social network effects on popularity biases in recommender systems. In RSWeb@ RecSys."},{"key":"e_1_3_2_6_2","volume-title":"SIGIR","author":"Ca\u00f1amares Roc\u00edo","year":"2017","unstructured":"Roc\u00edo Ca\u00f1amares and Pablo Castells. 2017. A probabilistic reformulation of memory-based collaborative filtering: Implications on popularity biases. In SIGIR."},{"key":"e_1_3_2_7_2","volume-title":"RecSys","author":"Chaney Allison J. B.","year":"2018","unstructured":"Allison J. B. Chaney, Brandon M Stewart, and Barbara E Engelhardt. 2018. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In RecSys."},{"key":"e_1_3_2_8_2","article-title":"Bias and debias in recommender system: A survey and future directions","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:2010.03240 (2020).","journal-title":"arXiv:2010.03240"},{"key":"e_1_3_2_9_2","article-title":"Semi-disentangled representation learning in recommendation system","author":"Chen Weiguang","year":"2020","unstructured":"Weiguang Chen, Wenjun Jiang, Xueqi Li, Kenli Li, Albert Zomaya, and Guojun Wang. 2020. Semi-disentangled representation learning in recommendation system. arXiv:2010.13282 (2020).","journal-title":"arXiv:2010.13282"},{"issue":"2","key":"e_1_3_2_10_2","article-title":"Disentangled item representation for recommender systems","volume":"12","author":"Cui Zeyu","year":"2021","unstructured":"Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu, and Liang Wang. 2021. Disentangled item representation for recommender systems. ACM Transactions on Intelligent Systems and Technology 12, 2 (2021).","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_11_2","unstructured":"Arthur Gretton Karsten M. Borgwardt Malte J. Rasch Bernhard Sch\u00f6lkopf and Alexander Smola. 2012. A kernel two-sample test. The Journal of Machine Learning Research 13 1 (2012) 723\u2013773."},{"key":"e_1_3_2_12_2","volume-title":"SIGKDD","author":"Grover Aditya","year":"2016","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD."},{"key":"e_1_3_2_13_2","volume-title":"ICLR","author":"Hassanpour Negar","year":"2019","unstructured":"Negar Hassanpour and Russell Greiner. 2019. Learning disentangled representations for counterfactual regression. In ICLR."},{"key":"e_1_3_2_14_2","volume-title":"SIGIR","author":"He Xiangnan","year":"2020","unstructured":"Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCM: Simplifying and powering graph convolution network for recommendation. In SIGIR."},{"key":"e_1_3_2_15_2","volume-title":"AAAI","author":"Hu Liang","year":"2014","unstructured":"Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, and Wei Cao. 2014. Deep modeling of group preferences for group-based recommendation. In AAAI, Vol. 28."},{"key":"e_1_3_2_16_2","volume-title":"ACL-IJCNLP","author":"Hu Linmei","year":"2020","unstructured":"Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, and Ming Zhou. 2020. Graph neural news recommendation with unsupervised preference disentanglement. In ACL-IJCNLP."},{"key":"e_1_3_2_17_2","volume-title":"RecSys","author":"Jamali Mohsen","year":"2010","unstructured":"Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In RecSys."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018699"},{"key":"e_1_3_2_19_2","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016).","journal-title":"arXiv:1609.02907"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.2307\/2332232"},{"key":"e_1_3_2_21_2","article-title":"Be causal: De-biasing social network confounding in recommendation","author":"Li Qian","year":"2021","unstructured":"Qian Li, Xiangmeng Wang, and Guandong Xu. 2021. Be causal: De-biasing social network confounding in recommendation. arXiv:2105.07775 (2021).","journal-title":"arXiv:2105.07775"},{"key":"e_1_3_2_22_2","volume-title":"Causation: Foundation to Application, Workshop at UAI. AUAI","author":"Liang Dawen","year":"2016","unstructured":"Dawen Liang, Laurent Charlin, and David M. Blei. 2016. Causal inference for recommendation. In Causation: Foundation to Application, Workshop at UAI. AUAI."},{"key":"e_1_3_2_23_2","volume-title":"CIKM","author":"Liu Ninghao","year":"2020","unstructured":"Ninghao Liu, Yong Ge, Li Li, Xia Hu, Rui Chen, and Soo-Hyun Choi. 2020. Explainable recommender systems via resolving learning representations. In CIKM."},{"key":"e_1_3_2_24_2","volume-title":"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 RecSys."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1177\/0049124193022001006"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"e_1_3_2_27_2","first-page":"1257","volume-title":"Advances in Neural Information Processing Systems","author":"Mnih Andriy","year":"2008","unstructured":"Andriy Mnih and Russ R. Salakhutdinov. 2008. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems. 1257\u20131264."},{"key":"e_1_3_2_28_2","unstructured":"Preksha Nema Alexandros Karatzoglou and Filip Radlinski. 2020. Untangle: Critiquing disentangled recommendations. (2020)."},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Judea Pearl and Elias Bareinboim. 2011. Transportability of causal and statistical relations: A formal approach. Proceedings of the AAAI Conference on Artificial Intelligence 25 1 (2011) 247\u2013254.","DOI":"10.1609\/aaai.v25i1.7861"},{"key":"e_1_3_2_30_2","article-title":"BPR: Bayesian personalized ranking from implicit feedback","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618 (2012).","journal-title":"arXiv:1205.2618"},{"key":"e_1_3_2_31_2","volume-title":"Diffusion of innovations","author":"Rogers Everett M.","year":"2014","unstructured":"Everett M. Rogers, Arvind Singhal, and Margaret M. Quinlan. 2014. Diffusion of innovations. Routledge."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3692-2"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"issue":"2","key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1037\/h0046506","article-title":"Anxiety, fear, and social isolation.","volume":"62","author":"Sarnoff Irving","year":"1961","unstructured":"Irving Sarnoff and Philip G. Zimbardo. 1961. Anxiety, fear, and social isolation. The Journal of Abnormal and Social Psychology 62, 2 (1961), 356.","journal-title":"The Journal of Abnormal and Social Psychology"},{"key":"e_1_3_2_35_2","article-title":"Disentangling multi-facet social relations for recommendation","author":"Sha Xiao","year":"2021","unstructured":"Xiao Sha, Zhu Sun, and Jie Zhang. 2021. Disentangling multi-facet social relations for recommendation. IEEE Transactions on Computational Social Systems (2021).","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/978-3-031-17849-8_21","volume-title":"International Conference on Similarity Search and Applications","author":"Sheth Paras","year":"2022","unstructured":"Paras Sheth, Ruocheng Guo, Kaize Ding, Lu Cheng, K. Sel\u00e7uk Candan, and Huan Liu. 2022. Causal disentanglement with network information for debiased recommendations. In International Conference on Similarity Search and Applications. Springer, 265\u2013273."},{"issue":"1","key":"e_1_3_2_37_2","article-title":"Social media marketing: The effect of information sharing, entertainment, emotional connection and peer pressure on the attitude and purchase intentions","volume":"5","author":"Sheth Sradha","year":"2017","unstructured":"Sradha Sheth and Jiyeon Kim. 2017. Social media marketing: The effect of information sharing, entertainment, emotional connection and peer pressure on the attitude and purchase intentions. GSTF Journal on Business Review (GBR) 5, 1 (2017).","journal-title":"GSTF Journal on Business Review (GBR)"},{"key":"e_1_3_2_38_2","article-title":"Comparing recommendations made by online systems and friends.","volume":"106","author":"Sinha Rashmi R.","year":"2001","unstructured":"Rashmi R. Sinha, Kirsten Swearingen, et\u00a0al. 2001. Comparing recommendations made by online systems and friends. DELOS 106 (2001).","journal-title":"DELOS"},{"key":"e_1_3_2_39_2","volume-title":"SIGIR","author":"Wang Xiang","year":"2020","unstructured":"Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled graph collaborative filtering. In SIGIR."},{"key":"e_1_3_2_40_2","volume-title":"SIGKDD","author":"Wei Tianxin","year":"2021","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 SIGKDD."},{"key":"e_1_3_2_41_2","volume-title":"CIKM","author":"Wu Qingyun","year":"2017","unstructured":"Qingyun Wu, Hongning Wang, Liangjie Hong, and Yue Shi. 2017. Returning is believing: Optimizing long-term user engagement in recommender systems. In CIKM."},{"issue":"5","key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"102608","DOI":"10.1016\/j.ipm.2021.102608","article-title":"Investigating and counteracting popularity bias in group recommendations","volume":"58","author":"Yalcin Emre","year":"2021","unstructured":"Emre Yalcin and Alper Bilge. 2021. Investigating and counteracting popularity bias in group recommendations. Information Processing & Management 58, 5 (2021), 102608.","journal-title":"Information Processing & Management"},{"key":"e_1_3_2_43_2","article-title":"Causal intervention for leveraging popularity bias in recommendation","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:2105.06067 (2021).","journal-title":"arXiv:2105.06067"},{"key":"e_1_3_2_44_2","volume-title":"WWW","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 WWW."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3582435","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3582435","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:48Z","timestamp":1750183728000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3582435"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,6]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,8,31]]}},"alternative-id":["10.1145\/3582435"],"URL":"https:\/\/doi.org\/10.1145\/3582435","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,6]]},"assertion":[{"value":"2022-03-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-23","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}