Extension of Direct Citation Model Using In-Text Citations
作者机构：Institute of ComputingKohat University of Science and TechnologyKohat26000Pakistan Department of Computer ScienceNAMAL InstituteMianwali42250Pakistan College of Computer Science and Information TechnologyAlbaha UniversityAl BahaSaudi Arabia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2021年第66卷第3期
页 面：3121-3138页
核心收录：
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：Direct citation model in-text citations frequencies normalized discount cumulative gain least square approximation
摘 要：Citations based relevant research paper recommendations can be generated primarily with the assistance of three citation models:(1)Bibliographic Coupling,(2)Co-Citation,and(3)Direct *** of new scholarly articles are published every *** flux of scientific information has made it a challenging task to devise techniques that could help researchers to find the most relevant research papers for the paper at *** this study,we have deployed an in-text citation analysis that extends the Direct Citation Model to discover the nature of the relationship degree-ofrelevancy among scientific *** this purpose,the relationship between citing and cited articles is categorized into three categories:weak,medium,and *** an experiment,around 5,000 research papers were crawled from the *** research papers were parsed for the identification of in-text citation ***,0.1 million references of those articles were extracted,and their in-text citation frequencies were computed.A comprehensive benchmark dataset was established based on the user ***,the results were validated with the help of Least Square Approximation by Quadratic Polynomial *** was found that degreeof-relevancy between scientific papers is a quadratic increasing/decreasing polynomial with respect to-increase/decrease in the in-text citation frequencies of a cited ***,the results of the proposed model were compared with state-of-the-art techniques by utilizing a well-known measure,known as the normalized Discount Cumulative Gain(nDCG).The proposed method received an nDCG score of 0.89,whereas the state-of-the-art models such as the Content,Bibliographic-coupling,and Metadata-based Models were able to acquire the nDCG values of 0.65,0.54,and 0.51 *** results indicate that the proposed mechanism may be applied in future information retrieval systems for better results.
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