基于知识图谱和图嵌入的个性化学习资源推荐

Personalized Learning Resource Recommendation Based on Knowledge Graph and Graph Embedding

面对海量的在线学习资源,学习者往往面临“信息过载”和“信息迷航”等问题,帮助学习者高效准确地获取适合自己的学习资源来提升学习效果,已成为研究热点.针对现有方法存在的可解释性差、推荐效率和准确度不足等问题,提出了一种基于知识图谱和图嵌入的个性化学习资源推荐方法,它基于在线学习通用本体模型构建在线学习环境知识图谱,利用图嵌入算法对知识图谱进行训练,以优化学习资源推荐中的图计算效率.基于学习者的学习风格特征进行聚类来优化学习者的资源兴趣度,以获得排序后的学习资源推荐结果.实验结果表明,相对于现有方法,所提方法能在大规模图数据场景下显著提升计算效率和个性化学习资源推荐的准确度.

Faced with numerous online learning resources,learners often suffer from information overload and information disorientation problems.It has become a hotspot to help learners efficiently and accurately obtain suitable learning resources to improve their learning effects.Considering the deficiencies of existing approaches,such as the poor interpretability as well as the limited efficiency and accuracy of recommendation,a new recommendation approach of personalized learning resources is proposed on the basis of knowledge graphs and graph embeddings.In this approach,a knowledge graph of the online learning environment is established through a generic ontology model,and the graph embedding algorithm is applied to train the knowledge graph for optimized efficiency of graph computation in learning resource recommendation.Then,the learners’interest in learning resources is optimized via clustering based on the learning style features of learners.Finally,the ranked recommendation results of learning resources are obtained.The experiments demonstrate that the proposed approach significantly improves the computational efficiency and the accuracy of personalized learning resource recommendations compared with existing methods in large-scale graph data scenarios.