Hybrid DeepFM Model with Attention and Meta-Learning for Enhanced Product Usage Prediction

Product usage prediction is a critical task for businesses aiming to personalize customer experiences and optimize decision-making processes. Traditional predictive models often struggle with capturing the complex, dynamic interactions present in large customer datasets. To address this, we propose an ensemble model that integrates DeepFM architecture, Attention Mechanism, and Model-Agnostic Meta-Learning (MAML). Our approach combines the strengths of deep learning, feature interaction modeling, and meta-learning to create a more adaptive and robust prediction framework. By leveraging attention mechanisms and rapid task adaptation through MAML, our model outperforms conventional methods, demonstrating improved accuracy and flexibility in predicting product usage patterns. This research presents a significant advancement in predictive modeling by providing a comprehensive, flexible solution that adapts effectively to varying customer behaviors.