Research on Personalized Learning Route Model Based on Improved Collaborative Filtering Algorithm

In order to solve the contradiction between the free course selection mode and blind course selection, this paper combines the knowledge-based recommendation algorithm model and the memory based collaborative filtering recommendation model, and proposes an improved collaborative filtering recommendation algorithm to mine the implicit learning order and association relationship between courses and provide recommendations for freshmen, This algorithm solves the cold start problem caused by the data sparsity problem of the traditional collaborative filtering recommendation algorithm, so that the algorithm can still give high-quality recommendation results when the initial data is extremely sparse. The experimental results show that compared with the traditional recommendation algorithm, the accuracy and recall of this method have been improved, good recommendation results are obtained on real data sets.