Multimodal AI-Powered Fashion Analysis and Recommendation System

The rapid growth of online fashion platforms has increased the need for intelligent and personalized recommendation systems. Traditional recommendation approaches such as collaborative filtering and content-based filtering often fail to capture complex user preferences and visual attributes effectively. This paper proposes a Multimodal AI-Based Fashion Analysis and Recommendation System that integrates both image and text inputs to improve recommendation accuracy and relevance. The system utilizes deep learning techniques to extract visual features from fashion images and semantic features from textual descriptions, combining them to generate meaningful product suggestions. Additionally, an Explainable Artificial Intelligence (XAI) component is incorporated to enhance transparency and user trust by providing reasoning behind recommendations. The system also includes a cloth segmentation module using UNet and a virtual try-on feature powered by the VITON-HD framework. The proposed approach aims to improve personalization, user engagement, and decision-making in online fashion platforms. .