论文标题
引用建议考虑内容和结构上下文嵌入
Citation Recommendations Considering Content and Structural Context Embedding
论文作者
论文摘要
近年来，要发表的学术论文数量正在成倍增加，并建议有足够的引用来帮助研究人员撰写论文是一项非凡的任务。传统方法可能不是最佳的，因为推荐的论文可能已经是用户知道的，或者与周围环境完全相关，而不是手稿中讨论的其他想法。在这项工作中，我们提出了一种新颖的嵌入算法doccit2vec，以及``结构背景''的新概念，以解决上述问题。所提出的方法在旨在模拟实际用法方案的广泛实验中表明了与基线模型相比基线模型的出色表现。
The number of academic papers being published is increasing exponentially in recent years, and recommending adequate citations to assist researchers in writing papers is a non-trivial task. Conventional approaches may not be optimal, as the recommended papers may already be known to the users, or be solely relevant to the surrounding context but not other ideas discussed in the manuscript. In this work, we propose a novel embedding algorithm DocCit2Vec, along with the new concept of ``structural context'', to tackle the aforementioned issues. The proposed approach demonstrates superior performances to baseline models in extensive experiments designed to simulate practical usage scenarios.
