Authors - Siddhi Mulewar, Abhijay Patil, Gauri Patil, Nikhil Chame, Smita Kulkarni Abstract - E-commerce has completely transformed traditional retail by lowering operating expenses and enabling worldwide access. Online shopping experiences have been further changed by the integration of artificial intelligence (AI) and machine learning (ML), especially with the advent of Fashion Recommendation Methods (FRM) that employ deep learning techniques. This research introduces a unique FRM that uses a single image input to provide tailored fashion suggestions based on user preferences, improving the quality of the shopping experience. Collaborative filtering (CF) is preferred method in this research work, which encourages users to explore a wider range of content and become more engaged. In this research work ResNet50 pre-trained neural networks proposed to extract information from photos, enabling precise and customized fashion recommendations. Comparative studies show that ResNet50 performs better than other CNN models, leading to increased personalization and accuracy. In the highly competitive world of e-commerce, this study emphasizes the potential of AI-driven suggestions to improve the online shopping experience, stimulate user engagement, and foster loyal consumers. VITON is a Virtual Try-On Network that uses images instead of 3D data to overlay clothes on a person’s image. It creates and refines photo-realistic images with natural clothing deformation using a coarse-to-fine strategy.
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