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 today's
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 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 we 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. We furthermore
critically discuss today's 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.

推荐系统中普遍存在的流行度偏差问题，导致推荐结果集中在热门物品，本文回顾了流行度偏差的潜在原因和现有方法，并批判性地讨论了当前文献的局限性。