  Recommendation and ranking systems are known to suffer from popularity bias;
the tendency of the algorithm to favor a few popular items while
under-representing the majority of other items. Prior research has examined
various approaches for mitigating popularity bias and enhancing the
recommendation of long-tail, less popular, items. The effectiveness of these
approaches is often assessed using different metrics to evaluate the extent to
which over-concentration on popular items is reduced. However, not much
attention has been given to the user-centered evaluation of this bias; how
different users with different levels of interest towards popular items are
affected by such algorithms. In this paper, we show the limitations of the
existing metrics to evaluate popularity bias mitigation when we want to assess
these algorithms from the users' perspective and we propose a new metric that
can address these limitations. In addition, we present an effective approach
that mitigates popularity bias from the user-centered point of view. Finally,
we investigate several state-of-the-art approaches proposed in recent years to
mitigate popularity bias and evaluate their performances using the existing
metrics and also from the users' perspective. Our experimental results using
two publicly-available datasets show that existing popularity bias mitigation
techniques ignore the users' tolerance towards popular items. Our proposed
user-centered method can tackle popularity bias effectively for different users
while also improving the existing metrics.
