A Revolutionary Deep Learning System for Clothes Suggestions: Convolutional Neural Networks (CNN) Feature Learning Combined with Generative AI and User Interaction Logs

Abstract

This study introduces a smart wardrobe assistant that uses deep learning, generative AI, and tailored user data to make context-aware clothing recommendations. Traditional recommendation systems use manual feature engineering or collaborative filtering, failing to account for aesthetic preferences, contextual considerations (weather, occasion, cultural norms), and changing trends. Convolutional neural networks (CNNs) for visual feature extraction, generative AI models for creative style reasoning, and real-time user interaction logs for tailored adaptation combine to overcome these constraints and progress the field. CNN models learn high-level semantic aspects like texture, form, color composition, and style from big fashion datasets. These attributes are then embedded into a multimodal representation that communicates with a generative transformer-based network to synthesize novel outfit combinations and recommend styles in various contexts. Click-through rates, dwell time, and explicit interaction log ratings help the algorithm match suggestions to taste profiles. The experimental results indicate considerable improvements in suggestion relevancy, user happiness, and engagement metrics above baseline methods. In addition, the system takes into account societal and weather-related events when making suggestions. Deep feature learning and adaptive generative reasoning greatly enhance the quality and usability of personalized fashion advise.