Design and development of a personalized niche perfume e-commerce web application
Nguyen, Tran Van Anh (2026)
2026
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202603275133
https://urn.fi/URN:NBN:fi:amk-202603275133
Tiivistelmä
Despite the rapid growth of digital commerce technologies, purchasing perfume online remains challenging due to the lack of sensory experience and limited fragrance information. Consumers often rely on brief descriptions or generic filtering systems, which may not adequately support confident decision-making. These limitations highlight the need for a more transparent and domain-specific recommendation approach in online fragrance retail.
This project implemented a full-stack e-commerce platform featuring a tailored perfume recommendation system. The application included a structured scent quiz and a rule-based scoring algorithm to generate personalized match score based on the collected user preferences and product attributes. Unlike generic recommendation engines, the system is designed to be clear and simple, ensuring that users understand the reasoning behind the suggestions without putting heavy load on the application.
The platform is developed using a React-based frontend, a Node.js and Express backend, and a MongoDB database, following a RESTful architecture. Secure authentication is implemented using JSON Web Tokens (JWT), and payment processing is handled through Stripe integration. The completed system demonstrates how domain-specific data modelling and tailored recommendation logic can improve user experience in niche perfume e-commerce.
This project implemented a full-stack e-commerce platform featuring a tailored perfume recommendation system. The application included a structured scent quiz and a rule-based scoring algorithm to generate personalized match score based on the collected user preferences and product attributes. Unlike generic recommendation engines, the system is designed to be clear and simple, ensuring that users understand the reasoning behind the suggestions without putting heavy load on the application.
The platform is developed using a React-based frontend, a Node.js and Express backend, and a MongoDB database, following a RESTful architecture. Secure authentication is implemented using JSON Web Tokens (JWT), and payment processing is handled through Stripe integration. The completed system demonstrates how domain-specific data modelling and tailored recommendation logic can improve user experience in niche perfume e-commerce.