Design and Implementation of Personalized Recommendation Algorithms in Adaptive Learning Systems

Abstract

This study addresses the issue of personalized recommendations in adaptive learning systems by proposing an algorithmic framework based on a hybrid recommendation strategy. By constructing a multi-dimensional learner model and integrating educational data mining and knowledge graph techniques, a resource recommendation algorithm that combines collaborative filtering and knowledge content has been designed. Additionally, a dynamic learning path generation method based on the A* algorithm has been implemented, incorporating reinforcement learning to optimize the path planning strategy. Experimental results demonstrate that this framework effectively enhances learning outcomes and user experience, exhibiting good scalability and practical value, thus providing reliable technical support for personalized learning in online education platforms.