Generative AI and Deep Learning Based Method Detecting Purchasers from Missing Data Social Media

The paper offers a cutting-edge technique that combines deep learning and generative AI to analyze social media networks and forecast user interaction with purchases. However, there is still a big problem: the lack of profile information for inactive members is a barrier. To forecast a user’s involvement with a brand, we provide a novel method that blends deep learning models and generative AI. The user experience, engagement, recommendation systems, content discovery, and revenue potential are all enhanced by this approach. In particular, we use Pre-trained Language Models (PaLMs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and a Convolutional Neural Network (CNN)-based model to categorize and forecast purchasers. We assess our algorithms using metrics such as accuracy, precision, recall, and F1-score on the Kaggle Social Network dataset, and we juxtapose our performance metrics with those of a novel technique. The results demonstrate the effectiveness of our proposed purchaser-prediction strategy.