摘要
【目的】提出一种文献被引量预测模型,用于发现潜在研究热点、优化改进刊物采编工作。【方法】综合考虑文献的关键词、作者、机构、国家、被引量等相关因素,利用图卷积进行特征提取,利用循环神经网络与注意力机制对被引量的时序信息与重要文献特征进行挖掘。【结果】利用Web of Science核心集中交通运输领域的文献对模型进行验证,与基准模型相比,在RMSE、MAE等各项指标上最大提升幅度达15.23%与16.91%。【局限】在所提模型的预训练步骤中,进行多次图卷积,使得算法的时间复杂度较高。【结论】本文所提模型将文献各项特征充分融合,极大提高了预测模型的性能。
[Objective]This paper proposes a citation prediction model for scholarly articles,which could identify potential research hot spots and optimize journal editing.[Methods]First,we used graph convolution to extract literature features,which include keywords,authors,institutions,countries,and citations.Then,we used recurrent neural network and attention model to examine the time-series information of citations and other features.[Results]We evaluated the proposed model with transportation articles from core journals indexed by the Web of Science.Compared with the benchmark model,our new method’s maximum improvements on RMSE and MAE were 15.23%and 16.91%.[Limitations]At the pre-training stage,our model adopted multiple graph convolutions,which was very time consuming.[Conclusions]The proposed model,which fully integrates literature features,could effectively predict their citations.
作者
张思凡
牛振东
陆浩
朱一凡
王荣荣
Zhang Sifan;Niu Zhendong;Lu Hao;Zhu Yifan;Wang Rongrong(School of Computer,Beijing Institute of Technology,Beijing 100081,China;Beijing Institute of Technology Library,Beijing 100081,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第9期56-67,共12页
Data Analysis and Knowledge Discovery
基金
国家重点研发计划基金项目“专业内容知识聚合服务技术研发与创新服务示范”(项目编号:2019YFB1406302)的研究成果之一。
关键词
被引量预测
图卷积
特征交叉
Citation Prediction
Graph Convolution
Feature Cross
