一种基于HIN的学习资源推荐算法研究

Study on the Recommendation Algorithm of Learning Resources Based on HIN

如何提高学习资源的推荐准确度是自适应学习研究中的核心问题.为此,本文提出了一种基于异构信息网络的学习资源推荐算法.该算法以基于元路径的相似性度量为基础,结合知识转化概率和学习反馈信息,计算学习者与所有学习资源之间的语义相似度,并依据该相似度进行排名,将排名top-K的学习资源推荐给学习者.相关的实验表明,本文方法有效实现了自适应学习中学习资源的准确推荐.

How to improve the recommendation accuracy of learning resources is the core problem in the study of adaptive learning.Therefore,this paper proposes a learning resource recommendation algorithm based on heterogeneous information network. The algorithm based on the similarity measure based on meta path,combined with feedback information,know ledge transformation probability and calculate semantic similarity between all learners and learning resources,and according to the similarity,will be ranked top-K learning resources recommended for learners. The relevant experiments show that this method is effective in realizing the accurate recommendation of learning resources in adaptive learning.