  Recently there has been a growing interest in fairness-aware recommender
systems, including fairness in providing consistent performance across
different users or groups of users. A recommender system could be considered
unfair if the recommendations do not fairly represent the tastes of a certain
group of users while other groups receive recommendations that are consistent
with their preferences. In this paper, we use a metric called miscalibration
for measuring how a recommendation algorithm is responsive to users' true
preferences and we consider how various algorithms may result in different
degrees of miscalibration. A well-known type of bias in recommendation is
popularity bias where few popular items are over-represented in
recommendations, while the majority of other items do not get significant
exposure. We conjecture that popularity bias is one important factor leading to
miscalibration in recommendation. Our experimental results using two real-world
datasets show that there is a strong correlation between how different user
groups are affected by algorithmic popularity bias and their level of interest
in popular items. Moreover, we show algorithms with greater popularity bias
amplification tend to have greater miscalibration.
