AI Fashion Recommendation System

A recommender system suggests items based on user preferences, and deep learning enhances its accuracy by handling large datasets efficiently. As online shopping grows, retailers seek intelligent methods to recommend clothing tailored to user interests, boosting sales. We propose an AI-powered, content-based fashion recommendation system using deep neural networks. Unlike traditional methods requiring manual feature extraction, our system automatically identifies product features, including clothing categories, streamlining the process. It also integrates gender classification to refine recommendations. Our model is tested with various machine learning algorithms, analyzing performance with and without demographic data. Results show that our system achieves lower loss compared to existing models and effectively addresses the cold start problem for new items. Additionally, it recommends novel, relevant, and unexpected fashion choices. By leveraging deep learning, our system enhances personalization, making fashion recommendations more accurate and engaging for online shoppers.