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Data"],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n            In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called \u201cexposure\u201d perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC (\n            <jats:italic>De-Bias Network Confounding in Recommendation<\/jats:italic>\n            ), inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network\u2019s perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines.\n          <\/jats:p>","DOI":"10.1145\/3533725","type":"journal-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T11:53:00Z","timestamp":1651751580000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["Be Causal: De-Biasing Social Network Confounding in Recommendation"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8308-9551","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"first","affiliation":[{"name":"School of Electrical Engineering Computing and Mathematical Sciences, Curtin University, Perth, WA, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3643-3353","authenticated-orcid":false,"given":"Xiangmeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Data Science and Machine Intelligence Lab, University of Technology Sydney, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1326-0859","authenticated-orcid":false,"given":"Zhichao","family":"Wang","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"Data Science and Machine Intelligence Lab, University of Technology Sydney, Sydney, NSW, Australia"}]}],"member":"320","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240360"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_3_3_4_2","first-page":"3207","article-title":"Counterfactual reasoning and learning systems: The example of computational advertising","volume":"14","author":"Charles Denis","year":"2013","unstructured":"Denis Charles, Max Chickering, and Patrice Simard. 2013. 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