Enhanced Collaborative Filtering Algorithm for Recommending Innovative Educational Resources in Higher Education Institutions

This paper introduces a new method for efficiently recommending educational resources based on an enhanced collaborative filtering algorithm (ECFA). First, this algorithm calculates the similarities of educational resources to cluster them, and then assesses users' ratings of educational resources in higher education institutions based on the clustering results; finally, a list of innovative educational resources is created based on the rate to enhance the educational resource recommendations. The experimental results show that the proposed algorithm converges quickly, indicating its high efficiency in generating recommendations. Moreover, the method achieves a low Mean Absolute Error (MAE) when recommending innovative educational resources for higher education institutions, suggesting high quality.