{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:36:05Z","timestamp":1775230565666,"version":"3.50.1"},"reference-count":145,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T00:00:00Z","timestamp":1707436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020AAA0106000"],"award-info":[{"award-number":["2020AAA0106000"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272262, and U23B2030"],"award-info":[{"award-number":["62272262, and U23B2030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100020721","name":"Guoqiang Institute, Tsinghua University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100020721","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by\n            <jats:italic>causality<\/jats:italic>\n            , not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.\n          <\/jats:p>","DOI":"10.1145\/3639048","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T21:58:06Z","timestamp":1704232686000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":90,"title":["Causal Inference in Recommender Systems: A Survey and Future Directions"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7561-5646","authenticated-orcid":false,"given":"Chen","family":"Gao","sequence":"first","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1837-6730","authenticated-orcid":false,"given":"Yu","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5199-1428","authenticated-orcid":false,"given":"Wenjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5828-9842","authenticated-orcid":false,"given":"Fuli","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8472-7992","authenticated-orcid":false,"given":"Xiangnan","family":"He","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-1659","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Bareinboim. 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