Published Paper Details:

Fashion recommendation systems are pivotal for enhancing online shopping using deep learning, specifically ResNet-50. Our Fashion Recommendation System (FRS) employs ResNet-50 to process and represent fashion images' key attributes like color and style. Content-based recommendations suggest visually similar items based on user preferences. The FRS evolves through machine learning, delivering improved user engagement and conversion rates in e-commerce fashion. This paper reviews and explores fashion recommendation systems and filtering techniques, filling a gap in academic literature. It serves as a valuable resource for machine learning, computer vision, and fashion retailing professionals. We trained our model using a large image dataset, including the multiple category labels, descriptions and high-res images of fashion products, which consists of 44k+ images. The results have been promising, indicating potential real-world applications such as searching for a product using its digital copy in large sets of images.