# Cross-language Citation Recommendation via Hierarchical Representation Learning on Heterogeneous Graph

## TL;DR

This paper introduces HRLHG, a hierarchical representation learning method on heterogeneous graphs, to improve cross-language citation recommendations by effectively embedding multilingual scholarly data.

## Contribution

It proposes a novel hierarchical random walk algorithm and a joint embedding space for multilingual publications, enhancing cross-language citation retrieval.

## Key findings

- Outperforms state-of-the-art baseline models.
- Improves interpretability of cross-language citation recommendations.
- Effectively captures multilingual relationships in scholarly data.

## Abstract

While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars. Unfortunately, because of language barrier, some scientists (especially the junior ones or graduate students who do not master other languages) cannot efficiently locate the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem. HRLHG can learn a representation function by mapping the publications, from multilingual repositories, to a low-dimensional joint embedding space from various kinds of vertexes and relations on a heterogeneous graph. By leveraging both global (task specific) plus local (task independent) information as well as a novel supervised hierarchical random walk algorithm, the proposed method can optimize the publication representations by maximizing the likelihood of locating the important cross-language neighborhoods on the graph. Experiment results show that the proposed method can not only outperform state-of-the-art baseline models, but also improve the interpretability of the representation model for cross-language citation recommendation task.