Exposure Fairness in Music Recommendation

As recommender systems play a larger and larger role in our interactions with online content, the biases that plague these systems grow in their impact on our content consumption and creation. This work focuses on the mitigation of one such bias, popularity bias, as it relates to music recommendation. We formulate the problem of music recommendation as that of automatic playlist continuation. In order to harness the power of graph neural networks (GNNs), we define our recommendation space as a bipartite graph with songs and playlists as nodes and edges between them indicating a song being contained in a playlist. Then, we implement PinSage, a state of the art graph based recommender system to perform link prediction. Finally, we integrate an individual fairness framework into the training regime of PinSage to learn fair representations which can be used to generate relevant recommendations.