Research on Real-time Medical Online Learning Content Recommendation based on Multi-view Data Mining

Abstract

The purpose of this paper is to solve the problem of intelligent analysis of learners' behavior and intelligent recommendation in the domain of medical online education. The teaching behavior has transformed from experience teaching into massive data teaching. Moreover, the learning behavior is also changed from centralized learning to fragmented learning. In this paper, we study the method of personal education recommendation to meet these challenges. In this paper, a novel multi-view extreme learning machine model is proposed. We can get the optimized classification results. Based on these results, we proposed a collaborative filtering based personal recommendation method and applied via Spark framework. The experimental results show that, based on the effective analysis of learning behavior, the proposed method can be used to recommend the medical online learning content for the learners in practical teaching. In this paper, data mining and recommendation methods are realized in the field of medical online education. The methodological research and case studies can meet the needs of medical online education.