Scientific writing builds upon already published papers. Manual identification of publications to read, cite or consider as related papers relies on a researcher's ability to identify fitting keywords or initial papers from which a literature search can be started. The rapidly increasing amount of papers has called for automatic measures to find the desired relevant publications, so-called paper recommendation systems. As the number of publications increases so does the amount of paper recommendation systems. Former literature reviews focused on discussing the general landscape of approaches throughout the years and highlight the main directions. We refrain from this perspective, instead we only consider a comparatively small time frame but analyse it fully. In this literature review we discuss used methods, datasets, evaluations and open challenges encountered in all works first released between January 2019 and October 2021. The goal of this survey is to provide a comprehensive and complete overview of current paper recommendation systems.
翻译:科学著作以已经出版的论文为基础; 将出版物作为相关论文阅读、引用或考虑的人工识别,取决于研究人员能否找到合适的关键词或初步论文,以便开始文献搜索; 迅速增加的论文数量要求采取自动措施,寻找所需的相关出版物,即所谓的纸质建议系统; 随着出版物数量的增加,纸质建议系统的数量也随之增加; 前文献审查侧重于讨论这些年来各种办法的总体情况,并突出主要方向; 我们从这个角度出发,不考虑一个相对较小的时间框架,而是充分加以分析; 在这次文献审查中,我们讨论了2019年1月至2021年10月首次发布的所有工作中采用的方法、数据集、评估和遇到的公开挑战; 本调查的目的是全面概述目前纸质建议系统。