Authors: Yi Zhao; Zhao Li; Bitao Li; Keqing He; Junfei Guo
Addresses: School of Computer, Wuhan University, Wuhan 430072, China ' Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (Ministry of Education), Hubei Key Laboratory of Intelligent Vision based Monitoring for Hydroelectric Engineering, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China ' College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China ' School of Computer, Wuhan University, Wuhan 430072, China ' School of Computer, Wuhan University, Wuhan 430072, China
Abstract: One of the tasks of scientific paper writing is to recommend. When the number of references is increased, there is no clear classification and the similarity measure of the recommendation system will show poor performance. In this work, we propose a novel recommendation research approach using classification, clustering and recommendation models integrated into the system. In an evaluation on ACL Anthology papers network data, we effectively use complex network of knowledge tree node degrees (refer to the number of papers) to enhance the accuracy of recommendation. The experimental results show that our model generates better recommended citation, achieving 10% higher accuracy and 8% higher F-score than to the keyword march method when the data is big enough. We make full use of the collective intelligence to serve the public.
Keywords: citation recommendation; classification; clustering; similarity; citation network.
International Journal of Computational Science and Engineering, 2019 Vol.19 No.4, pp.527 - 537
Received: 15 May 2016
Accepted: 09 Dec 2016
Published online: 30 Aug 2019 *