ABSTRACT: 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 (e.g., niche vs blockbuster-focused users) are affected by such algorithms. In this talk, I first give an overview of the popularity bias problem in recommender systems. Then, I show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users’ perspective and I propose a new metric that can address these limitations. In addition, I present an effective approach that mitigates popularity bias from the user-centered point of view. Finally, I 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. Using two publicly available datasets, I show that many of the existing popularity bias mitigation techniques ignore the users' tolerance towards popular items. The proposed user-centered method, on the other hand, can tackle popularity bias effectively for different users while also improving the existing metrics.