Fairness and Transparency in Music Recommender Systems: Improvements for Artists

Abstract

Music streaming services have become one of the main sources of music consumption in the last decade, with recommender systems playing a crucial role. Since these systems partially determine which songs listeners hear, they significantly influence the artists behind the music. However, when assessing the performance and fairness of music recommender systems, the perspectives of artists and others working in the music industry are often overlooked. Additionally, artists express a desire for greater transparency regarding why certain songs are recommended while others are not. This research project adopts a multi-stakeholder approach to close the gap between music recommender systems and the artists whose music they recommend. First, we gather insights from artists and music industry professionals through interviews and questionnaires. Building on those insights, we then aim to improve matching between end users and music from lesser-known artists by generating rich item and user representations. Results will be evaluated both quantitatively and qualitatively. Lastly, we plan to effectively communicate music recommender system fairness by increasing transparency for both end users and artists.