  Academic research in recommender systems has been greatly focusing on the
accuracy-related measures of recommendations. Even when non-accuracy measures
such as popularity bias, diversity, and novelty are studied, it is often solely
from the users' perspective. However, many real-world recommenders are often
multi-stakeholder environments in which the needs and interests of several
stakeholders should be addressed in the recommendation process. In this paper,
we focus on the popularity bias problem which is a well-known property of many
recommendation algorithms where few popular items are over-recommended while
the majority of other items do not get proportional attention and address its
impact on different stakeholders. Using several recommendation algorithms and
two publicly available datasets in music and movie domains, we empirically show
the inherent popularity bias of the algorithms and how this bias impacts
different stakeholders such as users and suppliers of the items. We also
propose metrics to measure the exposure bias of recommendation algorithms from
the perspective of different stakeholders.
