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Recent research has suggested different metrics to measure the inconsistencyof recommendation performance, including the accuracy difference between usergroups, miscalibration, and popularity lift. However, a study that relatesmiscalibration and popularity lift to recommendation accuracy across differentuser groups is still missing. Additionally, it is unclear if particular genrescontribute to the emergence of inconsistency in recommendation performanceacross user groups. In this paper, we present an analysis of these threeaspects of five well-known recommendation algorithms for user groups thatdiffer in their preference for popular content. Additionally, we study howdifferent genres affect the inconsistency of recommendation performance, andhow this is aligned with the popularity of the genres. Using data from LastFm,MovieLens, and MyAnimeList, we present two key findings. First, we find thatusers with little interest in popular content receive the worst recommendationaccuracy, and that this is aligned with miscalibration and popularity lift.Second, our experiments show that particular genres contribute to a differentextent to the inconsistency of recommendation performance, especially in termsof miscalibration in the case of the MyAnimeList dataset.

A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations