With the remarkable increase in the number of scientific entities such as publications, researchers, and scientific topics, and the associated information overload in science, academic recommender systems have become increasingly important for millions of researchers and science enthusiasts. However, it is often overlooked that these systems are subject to various biases. In this article, we first break down the biases of academic recommender systems and characterize them according to their impact and prevalence. In doing so, we distinguish between biases originally caused by humans and biases induced by the recommender system. Second, we provide an overview of methods that have been used to mitigate these biases in the scholarly domain. Based on this, third, we present a framework that can be used by researchers and developers to mitigate biases in scholarly recommender systems and to evaluate recommender systems fairly. Finally, we discuss open challenges and possible research directions related to scholarly biases.
翻译:随着出版物、研究人员和科学课题等科学实体数量的显著增加,以及科学信息超载,学术建议系统对数百万研究人员和科学爱好者越来越重要。然而,人们往往忽视,这些系统受到各种偏见的影响。在本篇文章中,我们首先打破学术建议系统的偏见,根据其影响和普遍程度对其进行描述。在这样做时,我们区分原先由人类造成的偏见和由推荐者系统引起的偏见。第二,我们概述了用来减轻学术领域这些偏见的方法。第三,我们提出了一个框架,供研究人员和开发者用来减轻学术建议系统中的偏见,并公平地评价推荐系统。最后,我们讨论了与学术偏见有关的公开挑战和可能的研究方向。