Affiliations: Shanghai Key Laboratory of Data Science, School of Software, Fudan University, Shanghai, China
Correspondence:
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Corresponding author: Zhaoxin Yu, Shanghai Key Laboratory of Data Science, School of Software, Fudan University, No. 220 Handan Road, Shanghai 200433, China. Tel.: +86 15121138803; Fax: +86 21 51355558; E-mail: [email protected].
Abstract: Researchers need to formulate their achievements as research papers. Representative references are essential to high-quality papers. Academic citation recommendation refers to providing the recommendation of citations for the author of papers when they write. With the help of citation recommendation, researchers can improve the efficiency of writing academic papers and reduce the omission of important related literature. To achieve this goal, some methods were proposed. Many of them used citation networks to learn the representation of papers and chose references, they tended to ignore the content properties of papers. There are also some methods used partial properties to recommend citation. But their performance can be further improved. In this paper, we propose a citation recommendation method based on context correlation. We use two neural network models to learn the representations of papers and their references, then calculate the context similarity of them. Besides, we also introduce the publishing time and authority of papers, two key properties of papers for citation evaluation. In the experiment section, we compare our method with other methods and evaluate the performance of different properties choice in our method, it shows that our method outperforms some baselines and the combination of the dimensions including time, authority and context performs better.
