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 ...
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.
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