  Recommendation algorithms are known to suffer from popularity bias; a few
popular items are recommended frequently while the majority of other items are
ignored. These recommendations are then consumed by the users, their reaction
will be logged and added to the system: what is generally known as a feedback
loop. In this paper, we propose a method for simulating the users interaction
with the recommenders in an offline setting and study the impact of feedback
loop on the popularity bias amplification of several recommendation algorithms.
We then show how this bias amplification leads to several other problems such
as declining the aggregate diversity, shifting the representation of users'
taste over time and also homogenization of the users experience. In particular,
we show that the impact of feedback loop is generally stronger for the users
who belong to the minority group.
