Artist biases in collaborative filtering for music recommendation
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- dc.contributor.author Ferraro, Andrés
- dc.contributor.author Jeon, Jea Ho
- dc.contributor.author Kim, Biho
- dc.contributor.author Serra, Xavier
- dc.contributor.author Bogdanov, Dmitry
- dc.date.accessioned 2020-07-24T08:15:14Z
- dc.date.available 2020-07-24T08:15:14Z
- dc.date.issued 2020
- dc.description Presentat a Machine Learning for Media Discovery Workshop, celebrat dins The 37th International Conference on Machine Learningca
- dc.description.abstract To evaluate if the recommendations are fair, we have to consider how all the stakeholders are affected. In this work, we focus on the artists in the music domain. We analyze the recommendations made with Collaborative Filtering from the artists’ side to understand how the recommender system can affect the artists’ reach and exposure. To this end, we group the artists using different aspects: location, gender, period, and type (e.g., solo, band, orchestra) and study the effect of the recommendations on these groups, comparing their distribution in recommendations, created by the system, with the previous activity of the listeners.en
- dc.description.sponsorship This research has been supported by Kakao Corp.
- dc.format.mimetype application/pdf
- dc.identifier.citation Ferraro A, Jeon JH, Kim B, Serra X, Bogdanov D. Artist biases in collaborative filtering for music recommendation. In: Proceedings of the 37 th International Conference on Machine Learning; 2020 Jul 13-18; Vienna, Austria. [Vienna]: ICML; 2020. [3 p.]
- dc.identifier.uri http://hdl.handle.net/10230/45185
- dc.language.iso eng
- dc.publisher ICML
- dc.relation.ispartof Proceedings of the 37 th International Conference on Machine Learning; 2020 Jul 13-18; Vienna, Austria. [Vienna]: ICML; 2020. [3 p.]
- dc.rights Copyright 2020 by the author(s).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/4.0/
- dc.subject.keyword Music recommender systemsen
- dc.subject.keyword Bias
- dc.subject.keyword Exposureen
- dc.subject.keyword Multi-stakeholder recommendationen
- dc.title Artist biases in collaborative filtering for music recommendation
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/publishedVersion