An Intelligent Recommendation System Using Market Segmentation

Title

An Intelligent Recommendation System Using Market Segmentation

Creator

Mechery S.; Preethi N.

Description

Electronic commerce, sometimes known as E-Commerce, is exchanging services and goods over the internet. These E-Commerce systems generate a lot of information. To solve these Data Overload issues, Recommender Systems are deployed. Because of the change to online buying, companies must now accommodate customers needs while also providing more options. The strategies and compromises of common recommender systems will be discussed to assist clients in these situations. Recommendation algorithms generate lists of things that the user have been previously using (content filtering) or develop recommendations and analyzing what items users purchase and identify similar target users (collaborative filtering). To assist clients in these situations, The Apriori algorithm, standard and custom metrics, association rules, aggregation, and pruning are used to improve results after a review of popular recommender system strategies that have been used. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Smart Innovation, Systems and Technologies, Vol-303 SIST, pp. 468-476.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Association rule mining; Content-based recommender system; E-Commerce; Hybrid recommender system; Market basket analysis; Market segmentation

Coverage

Mechery S., Department of Data Science, Christ University, Lavasa, Pune, India; Preethi N., Department of Data Science, Christ University, Lavasa, Pune, India

Rights

Restricted Access

Relation

ISSN: 21903018; ISBN: 978-981192718-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-2719-5_44

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130958120&doi=10.1007%2f978-981-19-2719-5_44&partnerID=40&md5=5bd16ae584af42e062697d6f7a2947ab