Citation Recommendation using Deep Canonical Correlation Analysis

TL;DR

This paper introduces a Deep CCA-based method for citation recommendation that captures complex relationships between textual and graph data, outperforming existing linear CCA approaches on large-scale datasets.

Contribution

The paper presents a novel Deep CCA approach for multi-modal citation recommendation, improving over linear CCA methods by modeling non-linear relationships between document representations.

Findings

Achieved over 11% improvement in MAP@10

Gained 5% in Precision@10

Enhanced ranking quality with 7% better Recall@10

Abstract

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.