Recommendation systems assist web users with personalized suggestions based on past preferences for products, or items including documents, books, movies, and research papers. The plethora and variety of research papers on the Web and digital libraries make it challenging for researchers to find relevant publications to their scholarly interests. To cope with this inevitable challenge, various models and algorithms have been proposed to assist researchers with personalized citation recommendations. Nevertheless, so far, no research study has exploited the validity and suitability of evaluations conducted for these models to find the most promising among them. This study investigates and examines the existing citation recommendation algorithms based on the following criteria: evaluation methods adopted, comparative baselines employed, the complexity of the proposed algorithm, reproducibility of the experimental results, and consistency and universality of the evaluation methods. Besides this, our study presents a generic architecture and process of a typical citation recommendation system and provides a brief overview of information filtering methods used in the existing models. The findings of the study have implications for researchers and practitioners working on research paper recommendation and related areas.

推荐系统根据过去对产品或项目（包括文档，书籍，电影和研究论文）的偏好，为网络用户提供个性化建议。网络和数字图书馆上的大量研究论文使研究人员很难找到符合其学术兴趣的相关出版物。为了应对这一不可避免的挑战，已经提出了各种模型和算法来协助研究人员提供个性化的引文建议。然而，到目前为止，还没有研究研究利用对这些模型进行评估的有效性和适用性来找到其中最有希望的模型。本研究根据以下标准调查并检查了现有的引文推荐算法：采用的评估方法，采用的比较基准，所提出算法的复杂性，实验结果的可重复性以及评估方法的一致性和通用性。除此之外，我们的研究还介绍了典型引文推荐系统的通用体系结构和过程，并简要概述了现有模型中使用的信息过滤方法。研究结果对从事研究论文推荐和相关领域研究的研究人员和从业者具有重要意义。