  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 for different users. In particular, we conjecture that
popularity bias which is a well-known phenomenon in recommendation is one
important factor leading to miscalibration in recommendation. Our experimental
results using two real-world datasets show that there is a connection between
how different user groups are affected by algorithmic popularity bias and their
level of interest in popular items. Moreover, we show that the more a group is
affected by the algorithmic popularity bias, the more their recommendations are
miscalibrated.
