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.

本文通过使用多个推荐算法和音乐、电影两个领域的公开数据集，实证了推荐算法的内在流行度偏差以及这种偏差对用户和项目供应商等不同利益相关者的影响，并提出了从不同利益相关者的角度衡量推荐算法暴露偏差的度量方法。