  Recommender systems are known to suffer from the popularity bias problem:
popular (i.e. frequently rated) items get a lot of exposure while less popular
ones are under-represented in the recommendations. Research in this area has
been mainly focusing on finding ways to tackle this issue by increasing the
number of recommended long-tail items or otherwise the overall catalog
coverage. In this paper, however, we look at this problem from the users'
perspective: we want to see how popularity bias causes the recommendations to
deviate from what the user expects to get from the recommender system. We
define three different groups of users according to their interest in popular
items (Niche, Diverse and Blockbuster-focused) and show the impact of
popularity bias on the users in each group. Our experimental results on a movie
dataset show that in many recommendation algorithms the recommendations the
users get are extremely concentrated on popular items even if a user is
interested in long-tail and non-popular items showing an extreme bias
disparity.
