Exploring artist gender bias in music recommendation

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• dc.contributor.author Shakespeare, Dougal
• dc.contributor.author Porcaro, Lorenzo
• dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
• dc.contributor.author Castillo, Carlos
• dc.date.accessioned 2020-09-30T11:54:16Z
• dc.date.available 2020-09-30T11:54:16Z
• dc.date.issued 2020
• dc.description Comunicació presentada a: Workshop on the Impact of Recommender Systems, ACM RecSys 2020 celebrat de manera virtual el 25 de setembre de 2020.
• dc.description.abstract Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users’ specific music preferences. Whilst accuracy metrics have been widely applied to evaluate recommendations in mRS literature, evaluating a user’s item utility from other impactoriented perspectives, including their potential for discrimination, is still a novel evaluation practice in the music domain. In this work, we center our attention on a specific phenomenon for which we want to estimate if mRS may exacerbate its impact: gender bias. Our work presents an exploratory study, analyzing the extent to which commonly deployed state of the art Collaborative Filtering (CF) algorithms may act to further increase or decrease artist gender bias. To assess group biases introduced by CF, we deploy a recently proposed metric of bias disparity on two listening event datasets: the LFM-1b dataset, and the earlier constructed Celma’s dataset. Our work traces the causes of disparity to variations in input gender distributions and user-item preferences, highlighting the effect such configurations can have on user’s gender bias after recommendation generation.
• dc.description.sponsorship This work is partially supported by the European Commission under the TROMPA project (H2020 770376).
• dc.format.mimetype application/pdf
• dc.identifier.citation Shakespeare D, Porcaro L, Gómez E, Castillo C. Exploring artist gender bias in music recommendation. In: Bogers T, Koolen M, Petersen C, Mobasher B, Tuzhilin A, Sar Shalom O, Jannach D, Konstan JA, editors. Proceedings of the Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems co-located with 14th ACM Conference on Recommender Systems (RecSys 2020); Sep 25; virtual. Aachen: CEUR Workshop Proceedings; 2020.
• dc.identifier.issn 1613-0073
• dc.identifier.uri http://hdl.handle.net/10230/45359
• dc.language.iso eng
• dc.publisher CEUR Workshop Proceedings
• dc.relation.ispartof Bogers T, Koolen M, Petersen C, Mobasher B, Tuzhilin A, Sar Shalom O, Jannach D, Konstan JA, editors. Proceedings of the Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems co-located with 14th ACM Conference on Recommender Systems (RecSys 2020); Sep 25; virtual. Aachen: CEUR Workshop Proceedings; 2020.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/770376
• dc.rights Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Gender bias
• dc.subject.keyword Bias disparity
• dc.subject.keyword Music recommendation
• dc.title Exploring artist gender bias in music recommendation
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion