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：随着科学出版物、研究人员及科学主题等学术实体的数量显著增长，以及随之而来的科学信息过载，学术推荐系统对数百万科研人员和科学爱好者而言日益重要。然而，这些系统易受多种偏见的影响常被忽视。本文首先解构学术推荐系统的偏见，并根据其影响程度和普遍性进行特征化分析。在此过程中，我们区分了由人类原生引发的偏见与推荐系统诱导产生的偏见。其次，我们综述了当前用于缓解学术领域这些偏见的方法。基于此，第三部分提出了一个可供研究人员和开发者使用的框架，用以缓解学术推荐系统中的偏见并公正地评估推荐系统。最后，我们探讨了与学术偏见相关的开放挑战及潜在研究方向。