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Popularity bias is the idea that a recommender system will unduly favorpopular artists when recommending artists to users. As such, they maycontribute to a winner-take-all marketplace in which a small number of artistsreceive nearly all of the attention, while similarly meritorious artists areunlikely to be discovered. In this paper, we attempt to measure popularity biasin 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 mostpopularity bias while less accurate models have less popularity bias. We alsofind no evidence of popularity bias in the commercial recommendations based ona simulated user experiment.

Exploring Popularity Bias in Music Recommendation Models and Commercial Steaming Services