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With the remarkable increase in the number of scientific entities such aspublications, researchers, and scientific topics, and the associatedinformation overload in science, academic recommender systems have becomeincreasingly important for millions of researchers and science enthusiasts.However, it is often overlooked that these systems are subject to variousbiases. In this article, we first break down the biases of academic recommendersystems and characterize them according to their impact and prevalence. Indoing so, we distinguish between biases originally caused by humans and biasesinduced by the recommender system. Second, we provide an overview of methodsthat have been used to mitigate these biases in the scholarly domain. Based onthis, third, we present a framework that can be used by researchers anddevelopers to mitigate biases in scholarly recommender systems and to evaluaterecommender systems fairly. Finally, we discuss open challenges and possibleresearch directions related to scholarly biases.

Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation