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.