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

TL;DR

This paper introduces a new method to model user preferences for both mainstream and low-mainstream artists, aiming to improve fairness in music recommendations by addressing data scarcity biases.

Contribution

It proposes a novel approach that captures diverse listening habits, enhancing recommendation fairness for users of unorthodox, low-mainstream artists.

Findings

Improved recommendation accuracy for low-mainstream artists.

Reduced bias towards popular artists in recommendations.

Enhanced user satisfaction across diverse music preferences.

Abstract

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