Recommendation of Academic Papers based on Heterogeneous Information Networks

Document Type

Conference Proceeding

Publication Date

11-1-2020

Abstract

The rapid advance in science and technology is made possible by research conduct and breakthroughs in a wide range of fields, which have resulted in a large number of academic papers. Searching through the enormous literature to find relevant information of one's research interest has become an increasingly important yet challenging problem for many researchers. Most existing methods for academic paper recommendation are based on the analysis of paper contents and only meet with limited success. We propose a novel method based on heterogeneous information networks for academic paper recommendation, referred to as HNPR. This method considers the citation relationship between papers, the collaboration relationship between authors, and the research area information of papers to construct two types of heterogeneous information networks. In such networks, a random walk-based strategy is used to simulate natural sentences for the discovery of relevance between two papers according to a mature natural language processing model. Extensive experimental results using real data in public digital libraries show that HNPR significantly improves the accuracy of academic paper recommendation in comparison with traditional content-based recommendation methods.

Identifier

85099792831 (Scopus)

ISBN

[9781728185774]

Publication Title

Proceedings of IEEE ACS International Conference on Computer Systems and Applications Aiccsa

External Full Text Location

https://doi.org/10.1109/AICCSA50499.2020.9316516

e-ISSN

21615330

ISSN

21615322

Volume

2020-November

Grant

2017YFB1400301

Fund Ref

National Key Research and Development Program of China

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