PPC
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)