To evaluate if the recommendations are fair, we
have to consider how all the stakeholders are affected.
In this work, we focus on the artists in the
music domain. We analyze the recommendations
made with Collaborative Filtering from the artists’
side to understand how the recommender system
can affect the artists’ reach and exposure. To this
end, we group the artists using different aspects:
location, gender, period, and type (e.g., solo, band,
orchestra) and study the effect of the recommendations
on ...
To evaluate if the recommendations are fair, we
have to consider how all the stakeholders are affected.
In this work, we focus on the artists in the
music domain. We analyze the recommendations
made with Collaborative Filtering from the artists’
side to understand how the recommender system
can affect the artists’ reach and exposure. To this
end, we group the artists using different aspects:
location, gender, period, and type (e.g., solo, band,
orchestra) and study the effect of the recommendations
on these groups, comparing their distribution
in recommendations, created by the system, with
the previous activity of the listeners.
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