A learning resource recommendation system based on knowledge graph and community elites

Inhalt:
In order to efficiently achieve accurate recommendation of learning resources, a collaborative filtering algorithm based on knowledge graph and community elites is established. We propose a big data-based service recommendation system to help learners select the content that best meets user needs from massive learning resources. The new system combines resource attribute information (knowledge graph) and user attribute information (community elite) to propose a rough multi-dimensional matrix model based on collaborative filtering algorithm. By constructing a simulated recommendation system, the method proposed in this paper is verified and compared, and finally it is proved that the method proposed in this paper is superior to the comparison method in performance.