  Recommender systems help people find relevant content in a personalized way.
One main promise of such systems is that they are able to increase the
visibility of items in the long tail, i.e., the lesser-known items in a
catalogue. Existing research, however, suggests that in many situations todays
recommendation algorithms instead exhibit a popularity bias, meaning that they
often focus on rather popular items in their recommendations. Such a bias may
not only lead to the limited value of the recommendations for consumers and
providers in the short run, but it may also cause undesired reinforcement
effects over time. In this paper, we discuss the potential reasons for
popularity bias and review existing approaches to detect, quantify and mitigate
popularity bias in recommender systems. Our survey, therefore, includes both an
overview of the computational metrics used in the literature as well as a
review of the main technical approaches to reduce the bias. Furthermore, we
critically discuss todays literature, where we observe that the research is
almost entirely based on computational experiments and on certain assumptions
regarding the practical effects of including long-tail items in the
recommendations.
