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Syst."],"published-print":{"date-parts":[[2023,12,31]]},"abstract":"<jats:p>Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually design each specific model for each specific confounder. However, real-world systems may include a huge number of confounders and thus designing each specific model for each specific confounder could be unrealistic. More importantly, except for those \u201cexplicit confounders\u201d that experts can manually identify and process such as item\u2019s position in the ranking list, there are also many \u201clatent confounders\u201d that are beyond the imagination of experts. For example, users\u2019 rating on a song may depend on their current mood or the current weather, and users\u2019 preference on ice creams may depend on the air temperature. Such latent confounders may be unobservable in the recorded training data. To solve the problem, we propose Deconfounded Causal Collaborative Filtering (DCCF). We first frame user behaviors with unobserved confounders into a causal graph, and then we design a front-door adjustment model carefully fused with machine learning to deconfound the influence of unobserved confounders. Experiments on real-world datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.<\/jats:p>","DOI":"10.1145\/3606035","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T11:55:16Z","timestamp":1687866916000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Deconfounded Causal Collaborative Filtering"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0865-5223","authenticated-orcid":false,"given":"Shuyuan","family":"Xu","sequence":"first","affiliation":[{"name":"Rutgers University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3646-933X","authenticated-orcid":false,"given":"Juntao","family":"Tan","sequence":"additional","affiliation":[{"name":"Rutgers University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8831-0753","authenticated-orcid":false,"given":"Shelby","family":"Heinecke","sequence":"additional","affiliation":[{"name":"Salesforce Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0630-3822","authenticated-orcid":false,"given":"Vena Jia","family":"Li","sequence":"additional","affiliation":[{"name":"Meta"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2633-8555","authenticated-orcid":false,"given":"Yongfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rutgers University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Multi-sided exposure bias in recommendation","author":"Abdollahpouri Himan","year":"2020","unstructured":"Himan Abdollahpouri and Masoud Mansoury. 2020. 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