Unfair Exposure of Artists in Music Recommendation

TL;DR

This paper investigates how popularity bias in music recommendation algorithms unfairly affects artists, especially less popular ones, highlighting the need for multi-stakeholder fairness considerations.

Contribution

It reveals the impact of popularity bias on artists in recommender systems, emphasizing fairness issues for content providers beyond users.

Findings

Different artist groups are systematically treated differently due to algorithm biases.

Popularity bias leads to over-recommendation of popular artists and neglect of less popular ones.

Biases in algorithms impact the fairness towards content providers in music recommendation.

Abstract

Fairness in machine learning has been studied by many researchers. In particular, fairness in recommender systems has been investigated to ensure the recommendations meet certain criteria with respect to certain sensitive features such as race, gender etc. However, often recommender systems are multi-stakeholder environments in which the fairness towards all stakeholders should be taken care of. It is well-known that the recommendation algorithms suffer from popularity bias; few popular items are over-recommended which leads to the majority of other items not getting proportionate attention. This bias has been investigated from the perspective of the users and how it makes the final recommendations skewed towards popular items in general. In this paper, however, we investigate the impact of popularity bias in recommendation algorithms on the provider of the items (i.e. the entities who are behind the recommended items). Using a music dataset for our experiments, we show that, due to some biases in the algorithms, different groups of artists with varying degrees of popularity are systematically and consistently treated differently than others.