Research on learning resource recommendation based on collaborative filtering algorithm

In the internet learning environment, the diversity of online learning platforms and the complexity of learning content make it difficult for learners to efficiently select suitable learning resources. Collaborative filtering algorithm, as a widely used recommendation technology, can effectively solve this problem. This article adopts a project-based collaborative filtering algorithm, using the Slope one algorithm to reduce data sparsity. By calculating the similarity between resources, a resource similarity matrix is constructed, and then weighted average is used for prediction scoring to recommend personalized learning resources for users.