Exploring Popularity Bias in Music Recommendation Models and Commercial Steaming Services

• URL: http://arxiv.org/abs/2208.09517v1
• Date: Fri, 19 Aug 2022 19:06:40 GMT
• Title: Exploring Popularity Bias in Music Recommendation Models and Commercial Steaming Services
• Authors: Douglas R. Turnbull and Sean McQuillan and Vera Crabtree and John Hunter and Sunny Zhang
• Abstract summary: Popularity bias is the idea that a recommender system will unduly favor popular artists when recommending artists to users. We measure popularity bias in three recommender system models (e.g., SLIM, Multi-VAE, WRMF) and on three commercial music streaming services (Spotify, Amazon Music, YouTube) We find that the most accurate model (SLIM) also has the most popularity bias while less accurate models have less popularity bias.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Popularity bias is the idea that a recommender system will unduly favor popular artists when recommending artists to users. As such, they may contribute to a winner-take-all marketplace in which a small number of artists receive nearly all of the attention, while similarly meritorious artists are unlikely to be discovered. In this paper, we attempt to measure popularity bias in three state-of-art recommender system models (e.g., SLIM, Multi-VAE, WRMF) and on three commercial music streaming services (Spotify, Amazon Music, YouTube). We find that the most accurate model (SLIM) also has the most popularity bias while less accurate models have less popularity bias. We also find no evidence of popularity bias in the commercial recommendations based on a simulated user experiment.

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