  Popularity bias is a well-known issue in recommender systems where few
popular items are over-represented in the input data, while majority of other
less popular items are under-represented. This disparate representation often
leads to bias in exposure given to the items in the recommendation results.
Extensive research examined this bias from item perspective and attempted to
mitigate it by enhancing the recommendation of less popular items. However, a
recent research has revealed the impact of this bias on users. Users with
different degree of tolerance toward popular items are not fairly served by the
recommendation system: users interested in less popular items receive more
popular items in their recommendations, while users interested in popular items
are recommended what they want. This is mainly due to the popularity bias that
popular items are over-recommended. In this paper, we aim at investigating the
factors leading to this user-side unfairness of popularity bias in recommender
systems. In particular, we investigate two factors: 1) the relationship between
this unfairness and users' interest toward items' categories (e.g., movie
genres), 2) the relationship between this unfairness and the diversity of the
popularity group in users' profile (the degree to which the user is interested
in items with different degree of popularity). Experiments on a movie
recommendation dataset using multiple recommendation algorithms show that these
two factors are significantly correlated with the degree of popularity
unfairness in the recommendation results.
