Style Matcher: A deep learning framework for visual fashion matching using ResNet50

During present times when digital transformation and personalization shape the market the fashion industry actively seeks powerful solutions for pushing user interest and style production. This research presents "Style Matcher" which operates as a fashion recommendation system through CNNs for generating tailored suggestions of clothing items and accessories. Users can input preferences about t shirts and glasses then the system retrieves fashion items with visually matching characteristics. The proposed system uses three core components which include data processing followed by CNN model training and recommendation score generation. The extracted features from fashion images by CNN models facilitate quick style matching operations. The model uses complex fashion item data to learn detailed style patterns while identifying visually similar fashion products. Through the new ranking system the system can compute precise and appropriate style recommendations by evaluating user preference similarities to suggested items. (Abstract)