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Exposure Fairness in Music Recommendation
Rebecca Salganik · Fernando Diaz · Golnoosh Farnadi

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in Women in Machine Learning »

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.

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Author Information

Rebecca Salganik (Universite de Montreal)
Fernando Diaz (Google)

Fernando Diaz is a research scientist at Google Brain Montréal. His research focuses on the design of information access systems, including search engines, music recommendation services and crisis response platforms is particularly interested in understanding and addressing the societal implications of artificial intelligence more generally. Previously, Fernando was the assistant managing director of Microsoft Research Montréal and a director of research at Spotify, where he helped establish its research organization on recommendation, search, and personalization. Fernando’s work has received awards at SIGIR, WSDM, ISCRAM, and ECIR. He is the recipient of the 2017 British Computer Society Karen Spärck Jones Award. Fernando has co-organized workshops and tutorials at SIGIR, WSDM, and WWW. He has also co-organized several NIST TREC initiatives, WSDM (2013), Strategic Workshop on Information Retrieval (2018), FAT* (2019), SIGIR (2021), and the CIFAR Workshop on Artificial Intelligence and the Curation of Culture (2019)

Golnoosh Farnadi (Mila)

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