Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery

TL;DR

This paper introduces a domain-aware fairness approach using graph neural networks to reduce popularity bias in music recommender systems, enhancing discovery of niche content while maintaining performance.

Contribution

It proposes a novel fairness-based method that aligns song representations with their sound similarity, improving niche content recommendation in GNN-based music systems.

Findings

Outperforms existing fairness benchmarks in cold start scenarios

Enhances recommendation of lesser-known music content

Robustly mitigates popularity bias in music discovery

Abstract

As online music platforms grow, music recommender systems play a vital role in helping users navigate and discover content within their vast musical databases. At odds with this larger goal, is the presence of popularity bias, which causes algorithmic systems to favor mainstream content over, potentially more relevant, but niche items. In this work we explore the intrinsic relationship between music discovery and popularity bias. To mitigate this issue we propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems. Our approach uses individual fairness to reflect a ground truth listening experience, i.e., if two songs sound similar, this similarity should be reflected in their representations. In doing so, we facilitate meaningful music discovery that is robust to popularity bias and grounded in the music domain. We apply our BOOST methodology to two discovery based tasks, performing recommendations at both the playlist level and user level. Then, we ground our evaluation in the cold start setting, showing that our approach outperforms existing fairness benchmarks in both performance and recommendation of lesser-known content. Finally, our analysis explains why our proposed methodology is a novel and promising approach to mitigating popularity bias and improving the discovery of new and niche content in music recommender systems.