In the age of online purchasing and e-commerce, tailored suggestions are essential for improving user experience and raising engagement. Specifically, fashion recommendation systems use deep learning algorithms to offer customers customized recommendations based on their browsing history and interests. This project describes the design and execution of a deep learning-based fashion recommendation system that uses the ResNet50 model.
The primary objective of this project is to build a system that can recommend fashion items to users based on their preferences and input images. The system aims to provide accurate and relevant recommendations by utilizing advanced deep-learning techniques, enhancing user satisfaction and engagement.
https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset
Based on user-input photographs, the Fashion Recommendation System shows promise in producing precise and pertinent fashion recommendations. The method effectively makes use of closest neighbors search to locate visually comparable fashion products and deep learning approaches to extract significant characteristics from photos. Because it allows users to interact with the system intuitively, the user interface improves accessibility and user engagement.
