文章基本信息

标题：Enhanced Recommendation Based on Article Level Management via Citation Networks
本地全文：下载
作者：P. Durga Devi ; C. Kanimozhi
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2017
卷号：6
期号：2
页码：2014
DOI：10.15680/IJIRSET.2017.0602103
出版社：S&S Publications
摘要：The main objective of this system is to develop a system that serves both purposes, leading readers tothe key literature in the areas of their interest, while also providing suggestions that may be as valuable as they are nonobvious.The recommendation system (based on a seed paper) needs to determine: (a) what papers are relevant to theseed paper, (b) of these, which are the most important and (c) for different user types. The scholarly literature isexpanding at a rate that necessitates intelligent algorithms for search and navigation. For the most part, the problem ofdelivering scholarly articles has been solved. If one knows the title of an article, locating it requires little effort and,paywalls permitting, acquiring a digital copy has become trivial. However, the navigational aspect of scientific search –finding relevant, influential articles that one does not know exist – is in its early development. In this paper, weintroduce Eigenfactor Recommends – a citation-based method for improving scholarly navigation. The algorithm usesthe hierarchical structure of scientific knowledge, making possible multiple scales of relevance for different users. Weimplement the method dynamically and generate many recommendations from user articles from various bibliographiccollections using server side scripting nature.
关键词：Citation Network; Reference Evaluation; Document Level Citation; Knowledge Discovery.