Tao Shaoyu, Shen Chaoyuan, Zhu Li, Dai Tao
School of Software Engineering, Xi'an Jiaotong University, Xi'an, Shanxi 710049, China.
Comput Intell Neurosci. 2020 Oct 22;2020:5343214. doi: 10.1155/2020/5343214. eCollection 2020.
Context-aware citation recommendation aims to automatically predict suitable citations for a given citation context, which is essentially helpful for researchers when writing scientific papers. In existing neural network-based approaches, overcorrelation in the weight matrix influences semantic similarity, which is a difficult problem to solve. In this paper, we propose a novel context-aware citation recommendation approach that can essentially improve the orthogonality of the weight matrix and explore more accurate citation patterns. We quantitatively show that the various reference patterns in the paper have interactional features that can significantly affect link prediction. We conduct experiments on the CiteSeer datasets. The results show that our model is superior to baseline models in all metrics.
上下文感知引用推荐旨在为给定的引用上下文自动预测合适的引用，这在研究人员撰写科学论文时非常有帮助。在现有的基于神经网络的方法中，权重矩阵中的过度相关性会影响语义相似性，这是一个难以解决的问题。在本文中，我们提出了一种新颖的上下文感知引用推荐方法，该方法可以从根本上提高权重矩阵的正交性并探索更准确的引用模式。我们定量地表明，论文中的各种引用模式具有可以显著影响链接预测的交互特征。我们在CiteSeer数据集上进行了实验。结果表明，我们的模型在所有指标上均优于基线模型。
