论文标题
探索音乐推荐模型和商业蒸汽服务中的受欢迎程度偏见
Exploring Popularity Bias in Music Recommendation Models and Commercial Steaming Services
论文作者
论文摘要
流行性偏见是，推荐系统在向用户推荐艺术家时会过分偏爱流行艺术家。因此，他们可能会为赢家全力市场做出贡献，在该市场中，少数艺术家几乎受到了所有关注，而同样不可能发现同样的有功艺术家。在本文中，我们试图在三种最先进的推荐系统模型（例如Slim，Multi-Vae，WRMF）和三种商用音乐流媒体服务（Spotify，Amazon Music，YouTube）中衡量普及性偏见。我们发现，最准确的模型（Slim）也具有最受欢迎的偏见，而准确的模型的流行性偏差较小。我们还没有根据模拟用户实验在商业建议中发现流行偏见的证据。
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.
