用深度非线性方法提升学术文献引用推荐准确率
Citation Recommendation using Deep Canonical Correlation Analysis
- 采用深度典型相关分析捕捉文本与图结构的复杂非线性关系
- 在DBLP数据集上提升11%的平均精度@10，效果优于传统方法
- 适合需要精准引文推荐的研究者与学术系统开发者
近年来，引用推荐通过多视角表征学习提升了准确性，有效融合了学术文档中的多种模态信息。然而，如何在保留各模态特性的前提下整合多视图数据仍具挑战。本文提出一种基于深度典型相关分析（DCCA）的新算法，相较传统线性CCA方法，利用神经网络捕捉科学文献文本与图结构表示间的非线性关系。在大规模DBLP引用网络数据集上的实验表明，该方法显著优于现有基于CCA的方法，在Mean Average Precision@10上提升超11%，Precision@10提升5%，Recall@10提升7%。这些改进体现了更相关的引文推荐和更高的排序质量，说明DCCA的非线性变换能生成比线性投影更具表现力的潜在表示。
原文摘要 · Abstract (English)
Recent advances in citation recommendation have improved accuracy by leveraging multi-view representation learning to integrate the various modalities present in scholarly documents. However, effectively combining multiple data views requires fusion techniques that can capture complementary information while preserving the unique characteristics of each modality. We propose a novel citation recommendation algorithm that improves upon linear Canonical Correlation Analysis (CCA) methods by applying Deep CCA (DCCA), a neural network extension capable of capturing complex, non-linear relationships between distributed textual and graph-based representations of scientific articles. Experiments on the large-scale DBLP (Digital Bibliography & Library Project) citation network dataset demonstrate that our approach outperforms state-of-the-art CCA-based methods, achieving relative improvements of over 11% in Mean Average Precision@10, 5% in Precision@10, and 7% in Recall@10. These gains reflect more relevant citation recommendations and enhanced ranking quality, suggesting that DCCA's non-linear transformations yield more expressive latent representations than CCA's linear projections.
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