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

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Bibliographic Details
Main Authors: Michael, Färber, Melissa, Coutinho, Shuzhou, Yuan
Format: Book
Language:English
Published: Springer 2023
Subjects:
scholarly recommender systems
Online Access:https://link.springer.com/article/10.1007/s11192-023-04636-2
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8332
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spelling oai:localhost:PNK-83322023-04-26T06:41:47Z Biases in scholarly recommender systems: impact, prevalence, and mitigation Michael, Färber Melissa, Coutinho Shuzhou, Yuan scholarly recommender systems CC BY 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. 2023-04-26T06:41:47Z 2023-04-26T06:41:47Z 2023 Book https://link.springer.com/article/10.1007/s11192-023-04636-2 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8332 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic scholarly recommender systems
spellingShingle scholarly recommender systems
Michael, Färber
Melissa, Coutinho
Shuzhou, Yuan
Biases in scholarly recommender systems: impact, prevalence, and mitigation
description CC BY
format Book
author Michael, Färber
Melissa, Coutinho
Shuzhou, Yuan
author_facet Michael, Färber
Melissa, Coutinho
Shuzhou, Yuan
author_sort Michael, Färber
title Biases in scholarly recommender systems: impact, prevalence, and mitigation
title_short Biases in scholarly recommender systems: impact, prevalence, and mitigation
title_full Biases in scholarly recommender systems: impact, prevalence, and mitigation
title_fullStr Biases in scholarly recommender systems: impact, prevalence, and mitigation
title_full_unstemmed Biases in scholarly recommender systems: impact, prevalence, and mitigation
title_sort biases in scholarly recommender systems: impact, prevalence, and mitigation
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s11192-023-04636-2
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8332
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score 8.891145

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