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In recommender systems, algorithmic confounding refers to the confounding that arises when a recommender's own outputs shape the user data it is later trained and evaluated on, which can homogenize behavior. This meaning comes from Chaney (2018) in Proceedings of the 12th ACM Conference on Recommender Systems.[1]
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