A Hybrid Learning Approach to Product Usage Prediction Using Attention-Driven DeepFM Networks and Meta-Learned Optimization

Accurate product usage prediction is essential for effective inventory management, demand forecasting, and strategic decision-making in various industries. This study introduces a novel hybrid learning approach that leverages Attention-Driven Deep Factorization Machine (DeepFM) networks integrated with meta-learned optimization strategies to enhance predictive performance. Traditional forecasting methods often struggle to capture complex interactions within high-dimensional and sparse datasets, leading to suboptimal decision-making. In contrast, the proposed framework combines the strengths of deep learning and factorization machines, enabling the model to simultaneously learn both low-order and high-order feature interactions. The Attention-Driven DeepFM architecture incorporates attention mechanisms to dynamically focus on the most relevant features in the input data, thereby improving interpretability and predictive accuracy. This aspect is particularly crucial in scenarios characterized by fluctuating consumer behaviors and external influences, where certain variables may hold greater significance at different times. Additionally, the integration of meta-learning strategies facilitates rapid adaptation to new tasks, allowing the model to generalize effectively across varying product categories and market conditions. Empirical evaluations were conducted using diverse datasets from retail and e-commerce sectors, comparing the performance of the proposed hybrid model against traditional forecasting methods and other machine learning techniques. The results demonstrate a substantial improvement in forecasting accuracy, as evidenced by various performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Furthermore, the model's ability to adapt to new data distributions was validated through rigorous meta-learning experiments, showcasing its robustness in dynamic environments. This research contributes to the existing body of knowledge on product usage forecasting by presenting a comprehensive framework that addresses the limitations of conventional approaches. The findings have significant implications for practitioners, offering a sophisticated tool for optimizing inventory levels, enhancing customer satisfaction, and driving strategic business decisions. Future research directions include exploring the integration of additional contextual factors and further refining the model's capabilities for real-time applications, thereby ensuring its relevance in an ever-evolving market landscape.