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.

本文研究学术推荐系统中存在的偏见问题，对这些偏见进行分析并提出了缓解偏见及公正评估的方法和框架。同时，讨论相关研究的挑战和方向。

学术推荐系统中的偏差：影响、普遍性和缓解

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

The number of scientific publications nowadays is rapidly increasing, causing
information overload for researchers and making it hard for scholars to keep up
to date with current trends and lines of work. Consequently, recent work on
applying text mining technologies for scholarly publications has investigated
the application of automatic text summarization technologies, including extreme
summarization, for this domain. However, previous work has concentrated only on
monolingual settings, primarily in English. In this paper, we fill this
research gap and present an abstractive cross-lingual summarization dataset for
four different languages in the scholarly domain, which enables us to train and
evaluate models that process English papers and generate summaries in German,
Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for
multilingual summarization and thoroughly benchmark different models based on a
state-of-the-art multilingual pre-trained model, including a two-stage
`summarize and translate' approach and a direct cross-lingual model. We
additionally explore the benefits of intermediate-stage training using English
monolingual summarization and machine translation as intermediate tasks and
analyze performance in zero- and few-shot scenarios.

本研究填补了前人研究的空白，提出了一个多语种的学术领域摘要数据集，基于此我们能够训练和评估处理英语论文并生成德语、意大利语、汉语和日语摘要的模型，同时分析了在零样本和少样本情况下训练的性能。