As the volume of scientific publications grows exponentially, researchers increasingly face difficulties in locating relevant literature. Research Paper Recommender Systems have become vital tools to mitigate this information overload by delivering personalized suggestions. This survey provides a comprehensive analysis of Research Paper Recommender Systems developed between November 2021 and December 2024, building upon prior reviews in the field. It presents an extensive overview of the techniques and approaches employed, the datasets utilized, the evaluation metrics and procedures applied, and the status of both enduring and emerging challenges observed during the research. Unlike prior surveys, this survey goes beyond merely cataloguing techniques and models, providing a thorough examination of how these methods are implemented across different stages of the recommendation process. By furnishing a detailed and structured reference, this work aims to function as a consultative resource for the research community, supporting informed decision-making and guiding future investigations in the advances of effective Research Paper Recommender Systems.

翻译：随着科学出版物数量呈指数级增长，研究人员在查找相关文献时面临的困难日益加剧。研究论文推荐系统通过提供个性化建议，已成为缓解这种信息过载的重要工具。本综述基于该领域先前的评述，对2021年11月至2024年12月期间开发的研究论文推荐系统进行了全面分析。它广泛概述了所采用的技术与方法、所使用的数据集、所应用的评价指标与流程，以及在研究过程中观察到的长期存在及新兴挑战的现状。与以往的综述不同，本综述不仅仅是对技术和模型的罗列，还对这些方法在推荐过程不同阶段的具体实施方式进行了深入剖析。通过提供一份详细且结构化的参考，本工作旨在成为研究界的一份咨询性资源，支持明智的决策制定，并指导未来在推进有效研究论文推荐系统方面的探索。