A Personalized E-Learning Services Recommendation Algorithm Based on User Learning Ability

Abstract

The E-learning services recommendation is essential in enabling precision instruction and personalized learning. In this paper, a new personalized E-learning services recommendation algorithm is proposed to solve the problem of low accuracy, recall and effectiveness. The algorithm builds user similarity matrix based on both user information data and user behavior data. In order to achieve the goal of bettering things, this paper creates an asymmetric similarity matrix based on the user learning ability and designs an E-learning services ranking strategy to make personalized E-learning service recommendation better. The application of the recommendation algorithm in the personalized E-learning platform of a software college shows that the new algorithm can improve the accuracy, recall and effectiveness compared with the traditional recommendation algorithm.