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Music recommender systems have become central parts of popular streamingplatforms such as Last.fm, Pandora, or Spotify to help users find music thatfits their preferences. These systems learn from the past listening events ofusers to recommend music a user will likely listen to in the future. Here,current algorithms typically employ collaborative filtering (CF) utilizingsimilarities between users' listening behaviors. Some approaches also combineCF with content features into hybrid recommender systems. While musicrecommender systems can provide quality recommendations to listeners ofmainstream music artists, recent research has shown that they tend todiscriminate listeners of unorthodox, low-mainstream artists. This is foremostdue to the scarcity of usage data of low-mainstream music as music consumptionpatterns are biased towards popular artists. Thus, the objective of our work isto provide a novel approach for modeling artist preferences of users withdifferent music consumption patterns and listening habits.

Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendations