论文标题
KL-MAT：通过信息几何形状进行公平的推荐系统
KL-Mat : Fair Recommender System via Information Geometry
论文作者
论文摘要
推荐系统存在固有的问题，例如稀疏性和公平性。尽管过去几十年来一直被广泛采用，但有关推荐算法的公平性研究直到最近才被大大忽视。解决问题的一个重要范式是正则化。但是，研究人员无法在其他领域（例如Lasso或Ridge Recression）中提出一个共识的正规化术语，例如正则化框架。在本文中，我们从信息几何形状借用概念，并提出了一种新的基于正则化的公平算法，称为KL-MAT。算法技术在MAE等准确性性能方面具有更强的性能。更重要的是，该算法比香草基质分解方法产生的结果要公平得多。 KL-MAT快速，易于实施且可解释。
Recommender system has intrinsic problems such as sparsity and fairness. Although it has been widely adopted for the past decades, research on fairness of recommendation algorithms has been largely neglected until recently. One important paradigm for resolving the issue is regularization. However, researchers have not been able to come up with a consensusly agreed regularization term like regularization framework in other fields such as Lasso or Ridge Regression. In this paper, we borrow concepts from information geometry and propose a new regularization-based fair algorithm called KL-Mat. The algorithmic technique leads to a more robust performance in accuracy performance such as MAE. More importantly, the algorithm produces much fairer results than vanilla matrix factorization approach. KL-Mat is fast, easy-to-implement and explainable.
