学习资源精准推荐模型及应用研究

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摘要:

根据属性特征推荐资源,由于存在冷启动和稀疏性问题,限制了在线学习资源推荐的性能.基于知识表示和协同过滤,将学习者的学习水平和学习风格等特征融入推荐过程,进行协同过滤个性化推荐,提出了一种学习资源精准推荐模型,构建了学习者和学习资源知识表示模型;通过实验表明知识表示-协同过滤相结合的推荐算法在个性化推荐和推荐准确度方面优于传统的CF算法.

Due to the existence of cold start and sparsity problems,the performance of online learning resource recommendation based on attribute characteristics is limited.This paper is based on knowledge representation and collaborative filtering.We integrate learners’learning levels and learning styles into the recommendation process and perform personalized recommendation based on collaborative filtering.Through the works of this paper,an accurate learning resource recommendation model is proposed,a knowledge representation model for learners and learning resources is built.Experiments show that the recommendation algorithm combined with knowledge representation and collaborative filtering outperforms the traditional CF algorithm in terms of personalized recommendation and recommendation accuracy.

作者:

王晓东时俊雅李淳吴慧萍

Wang Xiaodong;Shi Junya;Li Chun;Wu Huiping(College of Computer and Information Engineering,Henan Normal University,Xinxiang 453007,China)

机构地区:

betway官方app 计算机与信息工程学院

出处:

《betway官方app 学报：自然科学版》 CAS 北大核心 2019年第1期26-32,共7页

基金:

横向研究项目(5201119160004) betway官方app 研究生科研创新项目(YL201623)

关键词：

知识表示学习资源推荐协同过滤在线学习

knowledge representation learning resource recommendation collaborative filtering online learning