The number of academic papers being published is increasing rapidly, and recommending sufficient citations to assist researchers in writing papers is a non-trivial task. Conventional recommendation approaches may not be optimal, as the recommended papers may already be known to the users or may be solely relevant to the surrounding context but not to other concepts discussed in the manuscript. In this study, we propose a novel embedding algorithm, namely DocCit2Vec, along with the new concept of “structural context”, to address the aforementioned issues. The proposed models are compared extensively with network-based, document-based, and combined approaches in experiments of citation recommendation and classification tasks. Three implications are concluded. First, the document-based methods demonstrated overwhelmingly superior performances for citation recommendation than the network-based methods, as the latter lack consideration of the word information. Second, DocCit2Vec exhibited significant improvement for citation recommendation among the document-based methods. Third, the ability to conduct classification tasks could be significantly enhanced by adding attention layer to DocCit2Vec.

中文翻译：

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DocCit2Vec：通过嵌入内容和结构上下文的引文推荐

发表的学术论文数量正在迅速增加，推荐足够的引文来帮助研究人员撰写论文是一项不平凡的任务。传统的推荐方法可能不是最佳的，因为推荐的论文可能已经为用户所知，或者可能仅与周围的上下文相关，但与手稿中讨论的其他概念无关。在这项研究中，我们提出了一种新的嵌入算法，即 DocCit2Vec，以及“结构上下文”的新概念，以解决上述问题。在引文推荐和分类任务的实验中，所提出的模型与基于网络、基于文档和组合的方法进行了广泛的比较。得出三个影响。第一的，与基于网络的方法相比，基于文档的方法在引文推荐方面表现出压倒性的优势，因为后者缺乏对单词信息的考虑。其次，DocCit2Vec 在基于文档的方法中对引文推荐有显着改进。第三，通过向 DocCit2Vec 添加注意力层可以显着增强执行分类任务的能力。