Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks

doi:10.1016/j.knosys.2021.106744

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室

	Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks
	Zhu, Yifan 5; Lin, Qika 4; Lu, Hao 3,5; Shi, Kaize 5; Qiu, Ping 5; Niu, Zhendong 1,2,5
刊名	KNOWLEDGE-BASED SYSTEMS
	2021-03-05
卷号	215 页码:12
关键词	Paper recommendation Heterogeneous knowledge Attentive recurrent neural networks Recommendation systems E-learning
ISSN号	0950-7051
DOI	10.1016/j.knosys.2021.106744
通讯作者	Niu, Zhendong(zniu@bit.edu.cn)
英文摘要	Tremendous academic information causes serious information overload problems while supporting scientific research. Scientific paper and citation recommendation systems have been developed to relieve this problem and work as a filter to furnish only relevant papers to researchers. Although previous studies have made comparative progress, this problem is still challenging because current paper recommendation systems rely on heterogeneous and multi-sourced features, thereby requiring a unified learning representation to cover different types and modalities of information. Additionally, the implicit influence of scholars' previous preferences of writing and citing on his/her new manuscript has not been well considered in the previous studies. Facing the issue from these two aspects, in this paper, a heterogeneous knowledge embedding-based attentive RNN model is proposed to recommend scientific paper citations. First, the preparation of features consists of two parts: (1) building a unified learning representation of structural entities and relations for recommending paper citations; and (2) defining and constructing a bibliographic network comprising five types of entities and five relations. The bibliographic network enables learning a unified representation so that all graphical entities and relations can be vectorized using TransD. To establish textual representations, the PV-DM model is utilized to generate numeric features for the title of each paper. Second, by combining structural and textual representations focusing on the ``author-text query" scenario, an attentive bidirectional RNN is constructed to recommend paper and citation based on an user's identity with a length-limited inquiry to capture the scholars' previous writing and citing preferences, thereby reducing recommendation error. Through the DBLP dataset, our experiment results show the feasibility and effectiveness of our method, both in terms of the number as well as the quality of the first few recommended items. In specific, compared with existing models, our model has improved MRR and NDCG by approximately 4.8% and 2.4%, respectively. (C) 2021 Elsevier B.V. All rights reserved.
资助项目	National Key R&D Program of China[2019YFB1406303] ; National Key R&D Program of China[2019YFB1406302] ; National Natural Science Foundation of China[61370137] ; National Natural Science Foundation of China[61902016] ; Ministry of Education-China Mobile Research Foundation[2016/2-7] ; Beijing Institute of Technology[2017JYYJG-004] ; China's National Strategic Basic Research Program (973 Program)[2012CB720700]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000620459300015
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Ministry of Education-China Mobile Research Foundation ; Beijing Institute of Technology ; China's National Strategic Basic Research Program (973 Program)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44006]
专题	自动化研究所_复杂系统管理与控制国家重点实验室
通讯作者	Niu, Zhendong
作者单位	1.Beijing Inst Technol, Lib, Beijing 100081, Peoples R China 2.Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA 15260 USA 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian 710049, Peoples R China 5.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Yifan,Lin, Qika,Lu, Hao,et al. Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks[J]. KNOWLEDGE-BASED SYSTEMS,2021,215:12.
APA	Zhu, Yifan,Lin, Qika,Lu, Hao,Shi, Kaize,Qiu, Ping,&Niu, Zhendong.(2021).Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks.KNOWLEDGE-BASED SYSTEMS,215,12.
MLA	Zhu, Yifan,et al."Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks".KNOWLEDGE-BASED SYSTEMS 215(2021):12.