Research on personalised recommendation system for graphic design based on AIGC and big data

A personalized recommendation system for graphic design helps users quickly discover relevant and inspiring content aligned with their individual interests and creative activities. Traditional approaches, such as collaborative filtering, are widely adopted but suffer from data sparsity, cold start problems, limited contextual awareness, and poor adaptability to evolving user preferences. To address these limitations, this study proposes a hybrid recommendation framework that combines collaborative filtering with enhanced personalization driven by user interaction history and explicit feedback. The process begins with comprehensive data collection, including user generated designs and detailed interaction metadata. Preprocessing techniques, including resizing and normalization, ensure consistent input quality. Meaningful visual features are extracted using color histograms and layout analysis, while design styles are identified through Convolutional Neural Networks. Scalability for large datasets is achieved using a Hadoop based big data pipeline supported by MapReduce. Additionally, the system integrates an Artificial Intelligence Generated Content module trained with a fine tuned Generative Adversarial Network optimized using AdaGrad++. This component enhances creative generation and personalized recommendations. The proposed hybrid model effectively learns user preferences and delivers highly relevant graphic design suggestions. Experimental results demonstrate strong performance, achieving 98.25 percent accuracy, 99.25 percent recall, and a 98.75 percent F1 score. Overall.