论文标题
帕累托成对排名，以增强推荐系统的公平性
Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems
论文作者
论文摘要
自2010年左右引入以来，学习排名是一种有效的推荐方法。著名的算法（例如贝叶斯个性化的排名和协作较少的较少的过滤）对学术界和行业都产生了深远的影响。但是，大多数学习对方法的研究都集中在改善AUC，MRR和NDCG等技术准确性指标上。直到近年来，诸如公平性的推荐系统的其他评估指标都在很大程度上被忽略了。在本文中，我们提出了一种新的学习，以对名为Pareto成对排名的算法进行排名。我们的灵感来自贝叶斯个性化排名和权力法分配的想法。我们表明，在对技术准确性指标进行评估时，我们的算法与其他算法具有竞争力。更重要的是，在我们的实验部分中，我们证明，与其他9种当代算法相比，Pareto成对排名是最公平的算法。
Learning to rank is an effective recommendation approach since its introduction around 2010. Famous algorithms such as Bayesian Personalized Ranking and Collaborative Less is More Filtering have left deep impact in both academia and industry. However, most learning to rank approaches focus on improving technical accuracy metrics such as AUC, MRR and NDCG. Other evaluation metrics of recommender systems like fairness have been largely overlooked until in recent years. In this paper, we propose a new learning to rank algorithm named Pareto Pairwise Ranking. We are inspired by the idea of Bayesian Personalized Ranking and power law distribution. We show that our algorithm is competitive with other algorithms when evaluated on technical accuracy metrics. What is more important, in our experiment section we demonstrate that Pareto Pairwise Ranking is the most fair algorithm in comparison with 9 other contemporary algorithms.
