In this paper, we study the effect of popularity degradation bias in the context of local music recommendations. Specifically, we examine how accurate two top-performing recommendation algorithms, Weight Relevance Matrix Factorization (WRMF) and Multinomial Variational Autoencoder (Mult-VAE), are at recommending artists as a function of artist popularity. We find that both algorithms improve recommendation performance for more popular artists and, as such, exhibit popularity degradation bias. While both algorithms produce a similar level of performance for more popular artists, Mult-VAE shows better relative performance for less popular artists. This suggests that this algorithm should be preferred for local (long-tail) music artist recommendation.

本研究探讨了流行度下降偏差对本地音乐推荐的影响。具体而言，我们研究了两种表现优异的推荐算法——权重相关矩阵分解（WRMF）和多项式变分自编码器（Mult-VAE）在推荐艺术家时对艺术家流行度的准确性。研究发现，这两种算法在推荐流行艺术家方面都能提高推荐性能，因此存在流行度下降偏差。虽然这两种算法对流行艺术家具有相似水平的性能，但对于不太受欢迎的艺术家，Mult-VAE显示出更好的相对性能。这表明该算法适用于本地（长尾）音乐艺术家推荐。

本地音乐推荐中的流行度衰减偏差