Fashion Recommendation System

In the rapidly evolving landscape of e-commerce and fashion, the demand for efficient and personalized recommendation systems has grown significantly. This study presents a Fashion Recommendation System leveraging advanced image processing techniques to enhance visual search capabilities. The proposed system allows users to upload an image, initiating a search for visually similar fashion items within a vast database.  The methodology involves extracting essential features from uploaded images using state-of-the-art deep learning models, enabling the system to discern intricate details such as patterns, colors, and styles. These features are then used to compare and rank potential matches within the fashion inventory. The recommendation engine employs a combination of convolutional neural networks (CNNs) and similarity metrics to ensure accurate and diverse suggestions. The system not only enhances user experience by simplifying the search process but also addresses the limitations of traditional text-based recommendation systems. Through the integration of image processing, users can discover fashion items that align with their preferences in a more intuitive and visually appealing manner. The application of this system extends beyond e-commerce, finding relevance in fashion content platforms and social media. The Fashion Recommendation System presented in this study represents a novel approach to personalized visual search in the realm of fashion. The combination of image processing and deep learning techniques provides users with a seamless and engaging experience, fostering efficient exploration of diverse fashion choices within the ever-expanding online marketplace.

 KEYWORDS: convolutional neural networks (CNNs), ResNet50 and Mobile Net.