Popularity Degradation Bias in Local Music Recommendation
April Trainor Douglas Turnbull
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.