Exploring Artist Gender Bias in Music Recommendation

TL;DR

This study investigates whether popular collaborative filtering music recommendation algorithms amplify or reduce artist gender bias, using bias disparity metrics on real listening datasets to analyze underlying causes.

Contribution

It provides an exploratory analysis of how state-of-the-art collaborative filtering algorithms influence artist gender bias in music recommendations.

Findings

Bias disparity varies with input gender distributions.

User preferences significantly impact gender bias in recommendations.

Certain configurations can either increase or decrease artist gender bias.

Abstract

Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' specific music preferences. Whilst accuracy metrics have been widely applied to evaluate recommendations in mRS literature, evaluating a user's item utility from other impact-oriented perspectives, including their potential for discrimination, is still a novel evaluation practice in the music domain. In this work, we center our attention on a specific phenomenon for which we want to estimate if mRS may exacerbate its impact: gender bias. Our work presents an exploratory study, analyzing the extent to which commonly deployed state of the art Collaborative Filtering(CF) algorithms may act to further increase or decrease artist gender bias. To assess group biases introduced by CF, we deploy a recently proposed metric of bias disparity on two listening event datasets: the LFM-1b dataset, and the earlier constructed Celma's dataset. Our work traces the causes of disparity to variations in input gender distributions and user-item preferences, highlighting the effect such configurations can have on user's gender bias after recommendation generation.