Enhancing Product Usage Forecasting Through a Hybrid DeepFM Framework with Integrated Attention Mechanisms and Meta-Learning Strategies

In the rapidly evolving landscape of supply chain management and inventory optimization, accurate product usage forecasting remains a critical challenge for organizations aiming to enhance operational efficiency and customer satisfaction. This study presents a novel approach to product usage forecasting through the development of a Hybrid Deep Factorization Machine (DeepFM) framework, which integrates attention mechanisms and meta-learning strategies. The proposed framework is designed to capture complex patterns and interactions in historical usage data, thereby improving the predictive accuracy of product demand. The Hybrid DeepFM framework leverages the strengths of factorization machines, which are adept at modeling high-dimensional sparse data, alongside deep learning architectures that facilitate the extraction of intricate feature representations. By incorporating attention mechanisms, the model enhances its ability to focus on relevant features and temporal dynamics that significantly influence product usage. This aspect is particularly pertinent in scenarios characterized by seasonality and promotional events, where traditional forecasting methods often fall short. Additionally, the integration of meta-learning strategies enables the model to adapt quickly to new data distributions and varying product categories. This adaptability is crucial in a market characterized by rapid changes in consumer preferences and behavior. Through extensive experimentation on diverse datasets, including retail and e-commerce environments, the effectiveness of the proposed Hybrid DeepFM framework is empirically validated. The results demonstrate a significant improvement in forecast accuracy compared to baseline models, underscoring the potential of combining advanced machine learning techniques to address complex forecasting challenges. Furthermore, this study discusses the implications of enhanced forecasting accuracy for inventory management, resource allocation, and strategic planning, emphasizing the importance of data-driven decision-making in modern enterprises. By providing a comprehensive evaluation of the methodology and its applications, this research contributes to the existing body of knowledge on product usage forecasting and offers practical insights for practitioners seeking to implement advanced forecasting solutions in their organizations. In conclusion, the Hybrid DeepFM framework with integrated attention mechanisms and meta-learning strategies represents a significant advancement in the field of product usage forecasting. Its ability to adapt to diverse datasets and capture complex interactions positions it as a valuable tool for organizations aiming to optimize their forecasting processes and improve overall supply chain efficiency. Future research directions may include the exploration of additional hybrid models and the incorporation of external factors such as market trends and competitor actions to further enhance forecasting capabilities