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Fairness Through Domain Awareness: Mitigating Popularity Bias for Music Discovery

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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14611))

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Abstract

As online music platforms continue to 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 through the lens of individual fairness. We propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network 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 resistant 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 makes the case for the importance of domain-awareness when mitigating popularity bias in music recommendation.

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Notes

  1. 1.

    Github repository https://github.com/Rsalganik1123/Domain_Aware_ECIR2024.

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Acknowledgements

Funding support for project activities has been partially provided by Canada CIFAR AI Chair, Facebook award, IVADO scholarship, and NSERC Discovery Grants program. We also express our gratitude to Compute Canada for their support in providing facilities for our evaluations.

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Authors and Affiliations

  1. Université de Montreal/MILA, Montreal, QC, Canada

    Rebecca Salganik & Golnoosh Farnadi

  2. McGill University, Montreal, QC, Canada

    Golnoosh Farnadi

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Fernando Diaz

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  1. Rebecca Salganik
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  2. Fernando Diaz
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Correspondence to Rebecca Salganik .

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Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Salganik, R., Diaz, F., Farnadi, G. (2024). Fairness Through Domain Awareness: Mitigating Popularity Bias for Music Discovery. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14611. Springer, Cham. https://doi.org/10.1007/978-3-031-56066-8_27

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