  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. Then, we simulate the effect
of the system in the long term with a feedback loop. From this simulation we
can see how the system gives less opportunity to the majority of artists,
concentrating the users on fewer items. The results of our analysis demonstrate
the need for a better evaluation methodology for current music recommendation
algorithms, not only limited to user-focused relevance metrics.
