A Scientometric Analysis of Transient Patterns in Recommender System with Soft Computing Techniques

Charu Gupta, Amita Jain, Oscar Castillo, Nisheeth Joshi


Recommender systems recommend items to users based on their interests and have seen tremendous growth due to the use of internet and web services. Recommendation systems have seen escalating growth rate since late 1990’s. A query on Google Scholar (famous research based search engine) gives 175,000 articles for the query “recommender system”. With such a large database of research/application articles, there arises a need to analyses the data so as to fulfill the basic requirements of effectively understanding the potential of the quantum of literature available so far. The study focuses on the topic of recommender system with various soft computing techniques such as fuzzy logic, neural network and genetic algorithm. The major contribution of this work is the demonstration of progressive knowledge for domain visualization and analysis of recommender system with soft computing techniques. The analysis is supported by various scientometric indicators such as Relative Growth Rate (RGR), Doubling Time (DT), Co-Authorship Index (CAI), AuthorProductivity, Degree of Collaboration, Research Priority Index (RPI), Half Life, Country wise Productivity, Citation Analysis, Page Length Distribution, Source Contributors. This research presents first of its kind scientometric analysis on “recommender system with soft computing techniques”. The present work provides useful parameters for establishing relationships between quantifiable data and intangible contributions in the field of recommender systems.


Fuzzy logic, genetic algorithm, neural networks, recommender system, scientometric analysis, web of science

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