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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM46475

Savvyshoper - Smart Product Recommender and Price Comparison System Using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM46475

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

NH

Nilam Honmane1

GM

Gayatri Mhaisne2

GM

Gayatri Musmade3

ON

Omkar Narawde4

PN

Pratik Nayakwadi5

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Abstract

In today’s digital era, e-commerce has transformed the way consumers shop, offering a vast range of products across multiple platforms Although online shopping offers convenience, the variation in product prices and listings across platforms can cause confusion and inefficiencies for consumers trying to find the best deals. This work introduces an advanced e-commerce price comparison platform that leverages web scraping technologies to collect real-time product and pricing information from a variety of leading online marketplaces. By consolidating and interpreting this data, the system empowers users to make better-informed purchasing choices. Moreover, the solution integrates a recommendation engine that analyses users’ search behaviours and browsing patterns to deliver personalized product suggestions. Through the fusion of dynamic price comparison and intelligent recommendations, the platform seeks to simplify the online shopping journey, helping users efficiently locate affordable and relevant products.

KEYWORDS

Personalized Product Suggestion Price Comparison Web Scrapping E-commerce optimization

Cite This Article

Nilam Honmane1, Gayatri Mhaisne2, Gayatri Musmade3, Omkar Narawde4, Pratik Nayakwadi5, "Savvyshoper - Smart Product Recommender and Price Comparison System Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025.

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Publication Date

4/29/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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