A collaborative filtering and recommendation method for digital resources of Chinese language courses

A collaborative filtering and recommendation method for digital resources of Chinese language courses based on similarity algorithm is studied to solve the problem of low accuracy in recommending digital resources for Chinese language courses. Firstly, collect and normalise digital resource data. Secondly, the TF-IDF algorithm is used to calculate the feature weights of resource data. Then, learning preference features are extracted by calculating the amount of time students spend on web pages and combining that with collaborative filtering algorithms. Finally, a resource similarity matrix is constructed to achieve collaborative filtering and recommendation of digital resources for Chinese language courses based on the Pearson similarity algorithm. It has been proven that the recommendation accuracy of this method is higher than 95% through experiments, the recommendation time is lower, and the PR curve is closest to the upper right corner, indicating good recommendation performance.