期刊
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
卷 11, 期 1, 页码 591-599出版社
ATLANTIS PRESS
DOI: 10.2991/ijcis.11.1.44
关键词
Heterogeneous information; network embedding; personalized query-oriented reference paper recommendation; distributed representation
资金
- National Natural Science Foundation of China [61772429]
- China Postdoctoral Science Foundation [2017M613205, 2017M623241]
- Fundamental Research Funds for the Central Universities [3102016QD009, 3102016QD010]
- Basic Research Project - Shenzhen Science and Technology Innovation Committee [201703063000511]
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, interrelationships 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.
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