Popularity bias is the idea that a recommender system will unduly favor
popular artists when recommending artists to users. As such, they may
contribute to a winner-take-all marketplace in which a small number of artists
receive nearly all of the attention, while similarly meritorious artists are
unlikely to be discovered. In this paper, we attempt to measure popularity bias
in three state-of-art recommender system models (e.g., SLIM, Multi-VAE, WRMF)
and on three commercial music streaming services (Spotify, Amazon Music,
YouTube). We find that the most accurate model (SLIM) also has the most
popularity bias while less accurate models have less popularity bias. We also
find no evidence of popularity bias in the commercial recommendations based on
a simulated user experiment.

本文通过对三种最先进的推荐系统模型以及三种商用音乐流媒体服务的研究，尝试测量流行度偏见，发现准确度最高的模型（SLIM）也有最大的流行度偏见，而准确度较低的模型则有较少的流行度偏见。通过模拟用户实验，我们没有发现商业推荐存在流行度偏见的证据。