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Exploring Artist Gender Bias in Music Recommendation

  • Authors
  • Shakespeare D, Porcaro L, Gómez E, Castillo C
  • UPF authors
  • GOMEZ GUTIERREZ, EMILIA; CASTILLO OCARANZA, CARLOS ALBERTO ALEJANDRO; PORCARO, LORENZO;
  • Type
  • Articles de recerca
  • Journal títle
  • CEUR Workshop Proceedings
  • Publication year
  • 2020
  • Volume
  • 2697
  • Pages
  • 1-9
  • ISSN
  • 1613-0073
  • Publication State
  • Publicat
  • Abstract
  • (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.
  • Complete citation
  • Shakespeare D, Porcaro L, Gómez E, Castillo C. Exploring Artist Gender Bias in Music Recommendation. CEUR Workshop Proceedings 2020; 2697( ): 1-9.
Bibliometric indicators
  • Índex Scimago de 0.177 (2019)