论文标题
DeepFair：深度学习以改善推荐系统的公平性
DeepFair: Deep Learning for Improving Fairness in Recommender Systems
论文作者
论文摘要
推荐系统中缺乏偏见管理导致少数群体接受不公平的建议。此外，股权和精确度之间的权衡使得难以获得符合这两个标准的建议。在这里，我们提出了一种基于深度学习的协作过滤算法，该算法在公平和准确性之间提供最佳平衡的建议，而无需了解有关用户的人口统计信息。实验结果表明，可以在不失去相当大的准确性的情况下提出公平的建议。
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users. Experimental results show that it is possible to make fair recommendations without losing a significant proportion of accuracy.
