Authors :
K. Bhrameswara; Dr. Girish Kumar D.; M. M. Harshitha
Volume/Issue :
Volume 11 - 2026, Issue 5 - May
Google Scholar :
https://tinyurl.com/zj96rpne
Scribd :
https://tinyurl.com/2zsnk3y9
DOI :
https://doi.org/10.38124/ijisrt/26May147
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The rapid advancement of internet technologies and Advanced secure payment platforms enable significantly
transformed traditional commerce into modern e-commerce platforms. Although E-commerce delivers simplicity,
accessibility, and user-friendly experience a wide range of products, many existing e-commerce systems still lack
personalization and intelligent product discovery mechanisms. This paper presents MYNTHER, an intelligent e-commerce
website designed to enhance personalized features improve the online shopping experience product recommendations,
efficient catalog management, and secure transaction handling. The proposed system continuously analyzes Evaluates
customer actions, like their search and browsing history, search patterns, and purchase activity, to generate relevant and
timely recommendations. MYNTHER adopts a modular and scalable architecture that supports real-time operations,
efficient order processing, and future expansion. Experimental evaluation shows improved user engagement, reduced
product search time, and higher customer satisfaction when compared to conventional e-commerce platforms.
Keywords :
E-Commerce, Online Shopping, Personalization, Recommendation System, Web Application.
References :
- G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76–80, 2003.
- R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331– 370, 2002.
- K. C. Laudon and C. G. Traver, E-Commerce: Business, Technology, Society, Pearson Education, 2017.
- I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
- IEEE Xplore Digital Library, “https://ieeexplore.ieee.org,” accessed 2025.
The rapid advancement of internet technologies and Advanced secure payment platforms enable significantly
transformed traditional commerce into modern e-commerce platforms. Although E-commerce delivers simplicity,
accessibility, and user-friendly experience a wide range of products, many existing e-commerce systems still lack
personalization and intelligent product discovery mechanisms. This paper presents MYNTHER, an intelligent e-commerce
website designed to enhance personalized features improve the online shopping experience product recommendations,
efficient catalog management, and secure transaction handling. The proposed system continuously analyzes Evaluates
customer actions, like their search and browsing history, search patterns, and purchase activity, to generate relevant and
timely recommendations. MYNTHER adopts a modular and scalable architecture that supports real-time operations,
efficient order processing, and future expansion. Experimental evaluation shows improved user engagement, reduced
product search time, and higher customer satisfaction when compared to conventional e-commerce platforms.
Keywords :
E-Commerce, Online Shopping, Personalization, Recommendation System, Web Application.
