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.

中文翻译：

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SVD-CNN：基于SVD的具有正交约束的卷积神经网络模型，用于情境感知引文推荐

情境感知的引文建议旨在针对给定的引文环境自动预测合适的引文，这对于研究人员在撰写科学论文时实质上是有帮助的。在现有的基于神经网络的方法中，权重矩阵中的过度相关会影响语义相似度，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情境感知引文推荐方法，可以从本质上改善权重矩阵的正交性并探索更准确的引文模式。我们定量地显示了本文中的各种参考模式具有可以显着影响链接预测的交互功能。我们对CiteSeer数据集进行实验。结果表明，我们的模型在所有指标上均优于基线模型。