Enhancing E-Commerce Using Fashion Recommendation System

E-commerce faces difficulties in attaining personalized shopping experiences with which improved customer satisfaction and also increases sales. Traditional recommender systems often fail at giving appropriate predictions for user interest; this causes lower activity rates and even retention among the users. A recommended fashion e-commerce system is built here with the integration of the convolutional neural network technology that further enhances image-based analyses on the products. The user system has two modules here, the Admin and the User module. The Admin module manages inventory, tracks purchases, and maintains database integrity, and the user module provides an interface to browse products with personalized recommendations. The system analyzes product images and user behavior to provide accurate real-time suggestions by using CNNs along with collaborative filtering. This helps in better decision-making, reduces browsing time, and increases sales by targeted recommendations. The proposed system is therefore about revolutionizing the online shopping experience through the introduction of a smart, user-centric solution: thus, customer satisfaction and competitive advantage in e-commerce.