基于多重因素的个性化学习推荐系统.

In order to solve the problems existing in the learning recommendation algorithm, such as ignoring the analysis of the students' mastery of knowledge points and failing to probabilize the knowledge mastery, this paper proposed a recommendation method based on multiple factors. The method focused on the comprehensive weight of knowledge points, error rate and loss rate, and built a knowledge point mastery probability model, and applied the proposed strategy to implement an online personalized learning recommendation system. In terms of the systematic evaluation, through a survey of 200 high school students, the accuracy of the top-8 knowledge points recommended by this system achieves 91. 2% and F1 achieves 78. 4%. The results of the systematic survey reflect the effectiveness and reliability of the proposed strategy. [ABSTRACT FROM AUTHOR]