Bias and Feedback Loops in Music Recommendation: Studies on Record Label Impact

Abstract

We investigate the dimension of record labels in music recommendation datasets and study their impact on recommender systems. While music recommender systems research traditionally focuses on dimensions and metadata such as artist or genre, other dimensions such as popularity and gender have recently drawn increased interest. We argue that also the role of record labels deserves consideration in this process. To study their effect, we present a multi-stage web crawling approach that retrieves record label information for individual albums as well as an assignment to a major record company (Universal, Sony, Warner, or Independent). Using this information, we augment existing datasets to enable further analyses. We present analyses of record label diversity on two datasets, namely the Spotify Million Playlist Dataset and the LFM-2b dataset using Last.fm listening profiles. Based on the additional information, we can show different characteristics and identify particular biases. Additionally, we present the results of first experiments with regard to feedback loop simulation and the stability of record label distribution in the recommendation process.