  Popularity bias in music recommendation systems -- where artists and tracks
with the highest listen counts are recommended more often -- can also propagate
biases along demographic and cultural axes. In this work, we identify these
biases in recommendations for artists from underrepresented cultural groups in
prototype-based matrix factorization methods. Unlike traditional matrix
factorization methods, prototype-based approaches are interpretable. This
allows us to directly link the observed bias in recommendations for minority
artists (the effect) to specific properties of the embedding space (the cause).
We mitigate popularity bias in music recommendation through capturing both
users' and songs' cultural nuances in the embedding space. To address these
challenges while maintaining recommendation quality, we propose two novel
enhancements to the embedding space: i) we propose an approach to filter-out
the irrelevant prototypes used to represent each user and item to improve
generalizability, and ii) we introduce regularization techniques to reinforce a
more uniform distribution of prototypes within the embedding space. Our results
demonstrate significant improvements in reducing popularity bias and enhancing
demographic and cultural fairness in music recommendations while achieving
competitive -- if not better -- overall performance.
