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Published in:

Volume 7 Issue 9
September-2020
eISSN: 2349-5162

Unique Identifier

JETIR2009128

Page Number

948-957

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Title

A Comprehensive Study on Recommender Systems For E-Commerce Applications

ISSN

2349-5162

Cite This Article

"A Comprehensive Study on Recommender Systems For E-Commerce Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.948-957, September-2020, Available :http://www.jetir.org/papers/JETIR2009128.pdf

Abstract

Recommender Systems help consumers navigating through large product miscellany, making decisions in e-commerce environments and overcome information overload. These systems take the behavior, opinions and tastes of a large group of consumers into account and thus constitutes a social or collaborative recommendation approach. In contrast, content-based technique depends on product features and textual item descriptions. Knowledge-based technique, finally, produce item recommendations based on explicit knowledge models from the domain. Demographic technique purpose to categorize the consumer based on personal aspect and make recommendations based on demographic classes. Hybrid approach combines two or more techniques. Marginal utility is economic idea because economists and marketing research use it to discover how much of an item a consumer will purchase. Association rule mining technique concentrates on the mining of associations over sales data and help shop managers to analyze past transaction data and to improve their future business decisions and recommend products to a consumer on the basis of other consumers’ ratings for these products as well as the similarities between this consumer’s and other consumers’ tastes. This paper encapsulates subjective and objective parameter to design effective recommendation technique and also present model on cold start problem in e-commerce recommendation system The main idea behind the Recommender System for E-Commerce is to build relationship between the products (items), users (visitors/customers) and make decision to select the most appropriate product to a specific user. This system is used by the E-commerce websites to suggest products to their customers. Recommender Systems use Machine Learning algorithms such as Collaborative Filtering and Content Based Filtering. Collaborative Filtering also referred as social filtering, filters information by using the recommendations of other people. Content Based Filtering also referred as cognitive filtering recommend items based on the comparison between the content of the items and a user profile. In user point of view, Recommender Systems helps the user to take correct decision in their online transaction. It recommends the items to users such as books, electronic products and many other products in general. In manufacturer or retailer point of view, Recommender System increases the sales and users browsing experiences.

Key Words

Recommendation Systems, content-based, collaborative-based, hybrid-recommendations, E-commerce, evaluation metrics

Cite This Article

"A Comprehensive Study on Recommender Systems For E-Commerce Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 9, page no. pp948-957, September-2020, Available at : http://www.jetir.org/papers/JETIR2009128.pdf

Publication Details

Published Paper ID: JETIR2009128
Registration ID: 300816
Published In: Volume 7 | Issue 9 | Year September-2020
DOI (Digital Object Identifier):
Page No: 948-957
ISSN Number: 2349-5162

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Cite This Article

"A Comprehensive Study on Recommender Systems For E-Commerce Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 9, page no. pp948-957, September-2020, Available at : http://www.jetir.org/papers/JETIR2009128.pdf




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