Enhancing Predictive Accuracy in Product Usage Forecasting through a Meta-Learned Attention-Based DeepFM Framework

In the rapidly evolving landscape of product usage forecasting, the ability to accurately predict consumer behavior is critical for optimizing inventory management, enhancing marketing strategies, and improving customer satisfaction. This study introduces a novel meta-learned attention-based Deep Factorization Machine (DeepFM) framework designed to enhance predictive accuracy in product usage forecasting. The proposed approach leverages the strengths of deep learning and factorization machines, integrating attention mechanisms to dynamically focus on relevant features while adapting to new tasks through meta-learning techniques. The framework operates on two primary pillars: the attention mechanism, which assigns varying importance to different input features based on their relevance to the prediction task, and a meta-learning strategy that facilitates rapid adaptation to diverse datasets and evolving consumer behaviors. By employing curriculum learning principles, the model is trained progressively on simpler tasks before advancing to more complex scenarios, thereby improving its generalization capabilities in sparse data environments. Empirical validation was conducted using multiple real-world product usage datasets, including e-commerce transaction records and user engagement metrics. The results demonstrate that the meta-learned attention-based DeepFM framework significantly outperforms traditional predictive models in terms of accuracy, precision, recall, and F1-score. Furthermore, the attention scores provide valuable insights into feature relevance, enhancing the interpretability of the model and allowing stakeholders to make informed decisions based on the factors driving consumer behavior. This study contributes to the growing body of literature on predictive analytics by presenting a robust framework that effectively addresses the challenges of product usage forecasting. The findings underscore the potential of integrating attention mechanisms with meta-learning to improve predictive performance, offering a promising avenue for future research and practical applications in various domains, including marketing, inventory management, and customer relationship management.