Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations

TL;DR

This paper addresses popularity bias in music recommendation systems by analyzing and mitigating biases towards underrepresented cultural groups through novel embedding space enhancements, improving fairness without sacrificing performance.

Contribution

It introduces two innovative methods to reduce popularity bias in prototype-based matrix factorization, enhancing cultural fairness in music recommendations.

Findings

Significant reduction in popularity bias towards minority artists.

Improved demographic and cultural fairness in recommendations.

Maintained or improved overall recommendation quality.

Abstract

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.