Heterogeneous Information Network Embedding based Personalized Query-Focused Astronomy Reference Paper Recommendation

DOI

10.2991/ijcis.11.1.44How to use a DOI?

Keywords

Heterogeneous information; network embedding; personalized query-oriented reference paper recommendation; distributed representation

Abstract

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Xiaoyan Cai
AU  - Junwei Han
AU  - Shirui Pan
AU  - Libin Yang
PY  - 2018
DA  - 2018/01/22
TI  - Heterogeneous Information Network Embedding based Personalized Query-Focused Astronomy Reference Paper Recommendation
JO  - International Journal of Computational Intelligence Systems
SP  - 591
EP  - 599
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.44
DO  - 10.2991/ijcis.11.1.44
ID  - Cai2018
ER  -