基于学习风格的个性化自适应资源推荐算法研究

Research on Personalized Adaptive Resource Recommendation Algorithm Based on Learning Style

个性化自适应资源推荐是以学习者为中心、以人工智能和大数据技术为基础,模拟人类思维进行学习资源推荐的过程。论文在分析学习者和资源学习风格的基础上,分别构建学习者模型和资源模型,运用基于学习风格过滤推荐算法、协同过滤推荐算法、关联规则推荐算法,展开个性化自适应资源推荐研究。研究结果表明,以学习风格为基础的混合式自适应推荐的结果,更贴合学习者的个性化学习需求。

Personalized adaptive resource recommendation is a learner-centered process,which is based on artificial intelli-gence and big data technology,simulated human thinking to carry out learning resource recommendation.In this paper,the learner model and resource model are constructed separately on the basis of analyzing the learning styles of learners and resources,the re-search of recommendation algorithms on personalized and adaptive resource recommendation based on learning style filtering,col-laborative filtering and association rules,which used to carry out.Results show that hybrid adaptive recommendation based on learn-ing style are more in line with the personalized learning needs of learners.