Personalized Learning Resources Recommendation for Interest-Oriented Teaching

Abstract

An interest-oriented teaching method can stimulate students' interest in learning, thus generating great internal drive. The personalized learning resource recommendation method for interest-oriented teaching meets students' personalized learning preference needs, reduces students' learning resource selection cost, and provides students with more diversified and rational learning resource supply. Different from traditional recommendation algorithms, the recommendation algorithm constructed in this article essentially adjusts the recommendation results in real time based on college students' adoption behavior of historical recommendation information. This article describes the problem of personalized learning resource recommendation for interest-oriented teaching, and constructs a personalized learning resource recommendation model based on the communication power of high-scoring learning resources. Experimental results verify the effectiveness of the model.