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.

本文研究了音乐推荐系统的性别偏见现象并分析了其来源和影响，并使用偏差不平等指标在两个听音事件数据集上对整个现象进行了评估和研究。

探索音乐推荐中的艺术家性别偏见

Exploring Artist Gender Bias in Music Recommendation

Recommender systems are personalized: we expect the results given to a
particular user to reflect that user's preferences. Some researchers have
studied the notion of calibration, how well recommendations match users' stated
preferences, and bias disparity the extent to which mis-calibration affects
different user groups. In this paper, we examine bias disparity over a range of
different algorithms and for different item categories and demonstrate
significant differences between model-based and memory-based algorithms.

本文研究了推荐系统中的个性化、校准、偏差差异等主题，检验了不同算法和物品类别之间的显著差异。

升高声音：协作式推荐中的偏好偏差放大

Crank up the volume: preference bias amplification in collaborative  recommendation

Research on fairness in machine learning has been recently extended to
recommender systems. One of the factors that may impact fairness is bias
disparity, the degree to which a group's preferences on various item categories
fail to be reflected in the recommendations they receive. In some cases biases
in the original data may be amplified or reversed by the underlying
recommendation algorithm. In this paper, we explore how different
recommendation algorithms reflect the tradeoff between ranking quality and bias
disparity. Our experiments include neighborhood-based, model-based, and
trust-aware recommendation algorithms.

本篇研究探讨不同的推荐算法是如何权衡排序质量和偏见差异，特别是针对机器学习以及推荐系统中的公平性问题所做的实验。

协同推荐中的偏差差异：算法评估和比较

Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation  and Comparison

Recommender systems have been applied successfully in a number of different
domains, such as, entertainment, commerce, and employment. Their success lies
in their ability to exploit the collective behavior of users in order to
deliver highly targeted, personalized recommendations. Given that recommenders
learn from user preferences, they incorporate different biases that users
exhibit in the input data. More importantly, there are cases where recommenders
may amplify such biases, leading to the phenomenon of bias disparity. In this
short paper, we present a preliminary experimental study on synthetic data,
where we investigate different conditions under which a recommender exhibits
bias disparity, and the long-term effect of recommendations on data bias. We
also consider a simple re-ranking algorithm for reducing bias disparity, and
present some observations for data disparity on real data.

本研究在合成数据上进行了初步的实验研究，在不同条件下研究推荐系统如何表现出偏见失衡现象以及推荐的长期效果。我们考虑了一种简单的重新排名算法来减少偏见失衡，并对实际数据的数据失衡进行了一些观察。