Application of Artificial Intelligence Technologies in Blended E-Learning Platforms

Abstract

With the development of AI technologies, AI application in education has become increasingly important, especially in blended e-learning platforms. However, these platforms often face the problems of course recommendation accuracy and data sparsity. To this end, the study proposes an improved neural collaborative filtering algorithm that incorporates temporal information to enhance the performance of the recommendation system. The results show that in explicit feedback evaluation, the research algorithm improves 58.3% in root-mean-square error and 59.1% in mean absolute error over the traditional collaborative filtering algorithm. In the implicit feedback evaluation, the proposed algorithm achieves 0.44 at NDCG@10 and 0.5257 at HR@10, both of which are superior to the traditional collaborative filtering algorithm. It can be found that the research algorithm can effectively improve the accuracy and adaptability of the recommender system. The study not only improves the course recommendation effect on the blended e-learning platform, but also provides an important reference value on how to optimize the recommendation system using temporal information and artificial intelligence technology.