Personalized Combination Recommendation of Short Video Online Learning Resources

Abstract

Short video online learning resources are widely used in online learning classes because of their strong display ability, popularity and easiness to understand and concise content. The existing learning resource recommendation methods usually ignore the local time series dependence information and possible periodic changes of students' historic online learning rules, and are not suitable for the recommendation scenarios of short video online learning resources. Therefore, this article studies the personalized combination recommendation method of short video online learning resources. Preprocessing of short video online learning resources is carried out, including content feature analysis and candidate recommendation list generation. Then, based on the initial recommendation list, a combination recommendation model of short video online learning resources based on information enhancement and overlapping information of knowledge points is proposed, and the construction ideas of the model are described in detail. Experimental results verify the effectiveness of the model.