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Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>Traditional recommender systems aim to estimate a user\u2019s rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, causal inference has been introduced in recommendations to address the influence of unobserved confounders. Observing that confounders in recommendations are usually shared among items and are therefore multi-cause confounders, we model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. Specifically, to remedy the confounding bias, we estimate user-specific latent variables that render the item exposures independent Bernoulli trials. The generative distribution is parameterized by a DNN with factorized logistic likelihood and the intractable posteriors are estimated by variational inference. Controlling these factors as substitute confounders, under mild assumptions, can eliminate the bias incurred by multi-cause confounders. Furthermore, we show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional treatment space. Therefore, we theoretically demonstrate that controlling user features as pre-treatment variables can substantially improve sample efficiency and alleviate overfitting. Empirical studies on both simulated and real-world datasets demonstrate that the proposed deep causal recommender shows more robustness to unobserved confounders than state-of-the-art causal recommenders. Codes and datasets are released at https:\/\/github.com\/yaochenzhu\/Deep-Deconf.<\/jats:p>","DOI":"10.1145\/3653985","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T12:14:37Z","timestamp":1711455277000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep Causal Reasoning for Recommendations"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-2788","authenticated-orcid":false,"given":"Yaochen","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7776-1500","authenticated-orcid":false,"given":"Jing","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8712-326X","authenticated-orcid":false,"given":"Jiayi","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7882-1066","authenticated-orcid":false,"given":"Zhenzhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1145\/3240323.3240360","volume-title":"Proceedings of the 12th ACM Conference on Recommender Systems","author":"Bonner Stephen","year":"2018","unstructured":"Stephen Bonner and Flavian Vasile. 2018. 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In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1202\u20131212."},{"key":"e_1_3_3_32_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/1639714.1639717","volume-title":"Proceedings of the 3rd ACM Conference on Recommender Systems","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 Proceedings of the 3rd ACM Conference on Recommender Systems. 5\u201312."},{"issue":"528","key":"e_1_3_3_33_2","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1080\/01621459.2019.1689139","article-title":"Comment on \u201cblessings of multiple causes.\u201d","volume":"114","author":"Ogburn Elizabeth L.","year":"2019","unstructured":"Elizabeth L. Ogburn, Ilya Shpitser, and Eric J. Tchetgen Tchetgen. 2019. Comment on \u201cblessings of multiple causes.\u201d J. Amer. Statist. 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In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1679\u20131682."},{"key":"e_1_3_3_38_2","first-page":"1151","article-title":"Bayesianly justifiable and relevant frequency calculations for the applies statistician","author":"Rubin Donald B.","year":"1984","unstructured":"Donald B. Rubin. 1984. Bayesianly justifiable and relevant frequency calculations for the applies statistician. Ann. Stat. 12, 4 (1984), 1151\u20131172.","journal-title":"Ann. Stat."},{"issue":"3","key":"e_1_3_3_39_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/0378-3758(90)90077-8","article-title":"Formal model of statistical inference for causal effects","volume":"25","author":"Rubin Donald B.","year":"1990","unstructured":"Donald B. Rubin. 1990. Formal model of statistical inference for causal effects. J. Stat. Plan. Infer. 25, 3 (1990), 279\u2013292.","journal-title":"J. Stat. Plan. 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In Proceedings of the International Conference on Machine Learning. 1670\u20131679."},{"key":"e_1_3_3_42_2","doi-asserted-by":"crossref","unstructured":"Bernhard Scholkopf Francesco Locatello Stefan Bauer Nan Rosemary Ke Nal Kalchbrenner Anirudh Goyal and Yoshua Bengio. 2021. Toward causal representation learning. Proc. IEEE 109 5 (2021) 612\u2013634.","DOI":"10.1109\/JPROC.2021.3058954"},{"key":"e_1_3_3_43_2","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1145\/1835804.1835895","volume-title":"Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Steck Harald","year":"2010","unstructured":"Harald Steck. 2010. Training and testing of recommender systems on data missing not at random. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 713\u2013722."},{"key":"e_1_3_3_44_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1145\/2043932.2043957","volume-title":"Proceedings of the 5th ACM Conference on Recommender Systems","author":"Steck Harald","year":"2011","unstructured":"Harald Steck. 2011. Item popularity and recommendation accuracy. In Proceedings of the 5th ACM Conference on Recommender Systems. 125\u2013132."},{"key":"e_1_3_3_45_2","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1145\/2507157.2507160","volume-title":"Proceedings of the 7th ACM Conference on Recommender Systems","author":"Steck Harald","year":"2013","unstructured":"Harald Steck. 2013. Evaluation of recommendations: Rating-prediction and ranking. In Proceedings of the 7th ACM Conference on Recommender Systems. 213\u2013220."},{"key":"e_1_3_3_46_2","first-page":"1784","volume-title":"Proceedings of the 30th ACM International Conference on Information and Knowledge Management","author":"Tan Juntao","year":"2021","unstructured":"Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021. Counterfactual explainable recommendation. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 1784\u20131793."},{"issue":"528","key":"e_1_3_3_47_2","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1080\/01621459.2019.1686987","article-title":"The blessings of multiple causes","volume":"114","author":"Wang Yixin","year":"2019","unstructured":"Yixin Wang and David M. Blei. 2019. The blessings of multiple causes. J. Amer. Statist. Assoc. 114, 528 (2019), 1574\u20131596.","journal-title":"J. Amer. Statist. 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