Popularity Degradation Bias in Local Music Recommendation

• URL: http://arxiv.org/abs/2309.11671v1
• Date: Wed, 20 Sep 2023 22:36:33 GMT
• Title: Popularity Degradation Bias in Local Music Recommendation
• Authors: April Trainor and Douglas Turnbull
• Abstract summary: We study the effect of popularity degradation bias in the context of local music recommendations. We find that both algorithms improve recommendation performance for more popular artists. Mult-VAE shows better relative performance for less popular artists.
• Score: 0.13597551064547497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the effect of popularity degradation bias in the context of local music recommendations. Specifically, we examine how accurate two top-performing recommendation algorithms, Weight Relevance Matrix Factorization (WRMF) and Multinomial Variational Autoencoder (Mult-VAE), are at recommending artists as a function of artist popularity. We find that both algorithms improve recommendation performance for more popular artists and, as such, exhibit popularity degradation bias. While both algorithms produce a similar level of performance for more popular artists, Mult-VAE shows better relative performance for less popular artists. This suggests that this algorithm should be preferred for local (long-tail) music artist recommendation.

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