Heterogeneous information network embedding based personalized query-focused astronomy reference paper recommendation

Xiaoyan Cai, Junwei Han, Shirui Pan, Libin Yang

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference paper recommendation and propose a heterogeneous information network embedding based recommendation approach. In particular, we deem query researchers, query text, papers and authors of the papers as vertices and construct a heterogeneous information network based on these vertices. Then we propose a heterogeneous information network embedding (HINE) approach, which simultaneously captures intra-relationships among homogeneous vertices, inter-relationships among heterogeneous vertices and correlations between vertices and text contents, to model different types of vertices as vector formats in a unified vector space. The relevance of the query, the papers and the authors of the papers are then measured by the distributed representations. Finally, the papers which have high relevance scores are presented to the researcher as recommendation list. The effectiveness of the proposed HINE based recommendation approach is demonstrated by the recommendation evaluation conducted on the IOP astronomy journal database.

Original languageEnglish
Pages (from-to)591-599
Number of pages9
JournalInternational Journal of Computational Intelligence Systems
Issue number1
Publication statusPublished - Jan 2018
Externally publishedYes


  • Distributed representation
  • Heterogeneous information
  • Network embedding
  • Personalized query-oriented reference paper recommendation

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