A Deep-Learning-Based Dynamic Multidimensional Memory-Augmented Personalized Recommendation Research

To address the problem of inaccurate matching between personalized exercise recommendations and learners’ mastery of knowledge concepts/learning abilities, we propose the Dynamic Multidimensional Memory Augmented knowledge tracing model (DMMA). This model integrates a dynamic key-value memory neural network with the Ebbinghaus Forgetting Curve. By incorporating time decay factors and knowledge concept mastery speed factors, it dynamically adjusts knowledge update intensity, effectively resolving the insufficient personalized recommendation capabilities of traditional models. Experimental validation demonstrates its effectiveness: on Algebra 2006–2007, DMMA achieves 82% accuracy, outperforming CRDP-KT by 6%, while maintaining 53–55% accuracy for cold-start users (0–5 interactions), which is 25% higher than CoKT. The model’s integration of the Ebbinghaus forgetting curve and K-means-based concept classification enhances adaptability. Genetic algorithm optimization yields a diversity score of 0.79, with 18% higher 30-day knowledge retention. The FastDTW–Sigmoid hybrid similarity calculation (weight transition 0.27–0.88) ensures smooth cold-start adaptation, while novelty metrics reach 0.65 via random-forest-driven prediction. Ablation studies confirm component necessity: removing time decay factors reduces accuracy by 2.2%. These results validate DMMA’s superior performance in personalized education.