Artist and style exposure bias in collaborative filtering based music recommendations

Understand

Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles.

• In this on-going work we contribute to this research direction analyzing the impact of collaborative filtering recommendations from the perspective of artist and music style exposure given by the system.
• We first analyze the distribution of the recommendations considering the exposure of different styles or genres and compare it to the users' listening behavior.
• This comparison suggests that the system is reinforcing the popularity of the items.