Cross-lingual citations in English papers: a large-scale analysis of prevalence, usage, and impact

Saier, Tarek; Färber, Michael; Tsereteli, Tornike

doi:10.1007/s00799-021-00312-z

“…May (1997) also found serious English language bias in the Institute for Scientific Information (ISI) database, as most non-English journals are not covered by the Science Citation Index (SCI). Cross-lingual citations make up less than two percent of all citations (Saier et al, 2021). In the field of recommender systems, Torres et al (2004) observed that a recommender in a user's native language was greatly preferred to one in an alternative language, even if the items themselves recommended were in the alternative language (e.g., a Portuguese-based recommender recommending scientific papers in English).…”

Section: Unfairness Definitionmentioning

confidence: 99%

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

Färber¹,

Coutinho²,

Yuan³

2023

Preprint

0

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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.

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Biases in scholarly recommender systems: impact, prevalence, and mitigation

Färber

¹

,

Coutinho

²

,

Yuan

³

2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract