Recommender systems have been used since the beginning of the Web to assist users with personalized suggestions related to past preferences for items or products including books, movies, images, research papers and web pages. The availability of millions research articles on various digital libraries makes it difficult for a researcher to find relevant articles to his/er research. During the last years, a lot of research have been conducted through models and algorithms that personalize papers recommendations. With this survey, we explore the state-of-the-art citation recommendation models which we categorize using the following seven criteria: platform used, data factors/features, data representation methods, methodologies and models, recommendation types, problems addressed, and personalization. In addition, we present a novel k-partite graph-based taxonomy that examines the relationships among surveyed algorithms and corresponding k-partite graphs used. Moreover, we present (a) domain’s popular issues, (b) adopted metrics, and (c) commonly used datasets. Finally, we provide some research trends and future directions.

中文翻译：

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基于图的引文推荐模型分类法

自 Web 诞生以来，推荐系统就被用于帮助用户提供与过去对物品或产品（包括书籍、电影、图像、研究论文和网页）的偏好相关的个性化建议。各种数字图书馆上数百万篇研究文章的可用性使研究人员很难找到与其研究相关的文章。在过去的几年中，通过个性化论文推荐的模型和算法进行了大量研究。通过本次调查，我们探索了最先进的引文推荐模型，我们使用以下七个标准对其进行分类：使用的平台、数据因素/特征、数据表示方法、方法和模型、推荐类型、解决的问题和个性化. 此外，我们提出了一种新的基于 k 部分图的分类法，它检查所调查的算法与使用的相应 k 部分图之间的关系。此外，我们提出了 (a) 领域的流行问题，(b) 采用的指标，以及 (c) 常用的数据集。最后，我们提供了一些研究趋势和未来方向。