系统梳理2021-2024年论文推荐系统进展，助研究者高效找文献。
Recent Advances and Trends in Research Paper Recommender Systems: A Comprehensive Survey
- 覆盖2021至2024年论文推荐关键技术与流程
- 分析主流数据集与评估指标使用现状
- 适合关注科研效率与智能推荐的学者
随着科学出版物数量呈指数增长，研究人员在查找相关文献时面临日益严峻的信息过载问题。研究论文推荐系统已成为缓解这一困境的关键工具，能够提供个性化文献建议。本文全面综述了2021年11月至2024年12月间发展的研究论文推荐系统，基于已有综述进一步深化。系统梳理了所采用的技术方法、数据集、评估指标与流程，并总结了长期存在与新兴出现的研究挑战。与以往综述不同，本工作不仅列举技术模型，更深入剖析其在推荐全流程中的实际应用。通过提供结构化、详尽的参考框架，旨在为研究社区提供决策支持，推动高效论文推荐系统的发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。