WEB-BASED FASHION RECOMMENDATION SYSTEM USING DEEP LEARNING

Abstract

Over the past few years, there has been significant growth in both the textile and garment industiies. Millions of products are now available in online catalogues, saving customers from having to visit many stores, wait in queue or try on clothing in changing rooms. However, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. In addition to improving billions of customers' shopping experiences, Effective fashion RS can boost provider profits and sales. A person with no or little fashion sense will struggle to choose clothes that make a lasting impression. To solve this problem, this project aims to create an E-commerce website of Fashion Recommendation System namely “MASHAQ FASHION CLOTHING”, where user can get recommendations of similar items by detecting multiple products from the uploaded image. This project used YOLO algorithm for object detection and Siamese Network for recommending similar products. Also, there’s a Chat-bot for user enquiry. These systems have the potential to revolutionize the way we shop for fashion, making the process more efficient, enjoyable, and tailored to individual needs. In this era of data-driven decision making, the development of an advanced fashion recommendation system holds immense promise for both consumers and the fashion industry as a whole