Research Article Recommendation System with Survey Citation Network and Deep Learning Techniques

Abstract

The rapid growth in the field of science and technology has helped increase in newlinethe involvement of individuals in researches in their respective fields. Global availability of newlinethe published research articles is an important factor that explains the attraction for newlineresearchers to make contribution to their domains of interest. As a result, there is an newlineexponential increase in the number of research articles published every year, making newlinenovelties in research paper a difficult proportion in view of the requirement of collection of newlineinformation relating to the topics of interest, for keeping at least with matters that occur newlinearound, more particularly for identification of gaps in the topic of interest. Collection of newlinerelevant articles with the reservation of good quality research forms the first stage in any newlineresearch work - a time-consuming job. Research article recommendation systems find use in newlinethe support to researchers in the scenario and for economy in that time required in the search newlinefor relevant articles with focus on economy in time and effort. Any such system performs newlineanalysis of behavioral pattern of researchers in the performance of recommendations. The newlineresearch article recommendation system analyzes the behavioral patterns of individual newlineresearchers to perform recommendations. The focus of this research work is on exploration newlineof different type of the existing frameworks for getting an understanding of the working of newlinethese systems, dealing with various deep learning technologies and citation networks for newlineensuring presentation of highly relevant articles with innovative ideas for intimation of newlineinterest to other researchers newline