volume 12, issue 2, P1-16 2021
DOI: 10.1145/3437910
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Masoud Mansoury, Robin Burke, Bamshad Mobasher

Abstract: It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for the central tendency of users’ distributions. In this work, we demonstrate that a lack of flatness in rating distributions is neg…

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