  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.
