Fairness Through Domain Awareness: Mitigating Popularity Bias For Music
  Discovery
Rebecca Salganik Fernando Diaz Golnoosh Farnadi
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.