A Review on Recommendation Systems Based On Fuzzy Logics in E-Commerce

Nivedita Das¹

Section:Review Paper, Product Type: Journal-Paper
Vol.9 , Issue.3 , pp.22-31, Jun-2021

Online published on Jun 30, 2021

Copyright © Nivedita Das . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Recommender systems have turned into a significant web-based recommendation methodology and are popularly used to endorse various items. Huge amounts of data are available on the internet on the web, the need for analyzing and personalizing systems is continuously increasing. The recommendation systems have a vast range of applications in the field of e-commerce. This paper discusses the types of recommender systems based on fuzzy logic, and adaptive and flexible methods are specifically grouped into three clusters: collaborative filtering, content-based filtering, and hybrid filtering. This paper also addresses recommender system growth following the –eCommerce sector challenges. Each approach has its relative strengths and weaknesses relating to the domain. The main aim of this review paper is to analyze the different types of recommendation systems along with their techniques based on fuzzy logic and used in e-commerce.

Key-Words / Index Term :
Recommendation system (RS), E-commerce, Fuzzy logics, user behavior data, filtering, behavioral matrix

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