A static-dynamic feature fusion framework based on long short-term memory and collaborative filtering

Focusing on the recommendation and decision support module in management systems, this framework takes users’ basic information (e.g., demographics, cause-related attributes) and auxiliary data (e.g., temporal activity records, functional assessment data) as inputs. A loss function is employed to optimize model parameters, enabling the deep learning model to capture temporal patterns in service processes and mine key factors for training program decision-making. Methodologically, the framework integrates weighted matrix factorization to extract latent features from users’ static attributes while leveraging Long Short-Term Memory (LSTM) networks to model temporal dependencies from sequential auxiliary data, achieving a static-dynamic feature fusion that enables more precise personalization. By fusing LSTM's capability to process sequential data with collaborative filtering's strength in mining user-item correlations, the model not only achieves accurate personalized training program recommendations but also provides data-driven decision support for staff to formulate adaptive plans. Experimental validation demonstrates that the proposed framework enhances the precision and timeliness of training program recommendations, strengthens decision-making efficiency for staff, and improves the effectiveness of target service outcomes. Moreover, the model offers quantifiable predictions of functional outcomes alongside recommended programs, providing staff with actionable references for decision-making.