Recommender System for E-learning Based on Semantic Relatedness of Concepts
Digital publishing resources contain a lot of useful and authoritative knowledge.It may be necessary to reorganize the resources by concepts and recommend the related concepts for e-learning.A recommender system is presented in this paper based on the semantic relatedness of concepts computed by texts from digital publishing resources.Firstly,concepts are extracted from encyclopedias.Information in digital publishing resources is then reorganized by concepts.Secondly,concept vectors are generated by skip-gram model and semantic relatedness between concepts is measured according to the concept vectors.As a result,the related concepts and associated information can be recommended to users by the semantic relatedness for learning or reading.History data or users ’ preferences data are not needed for recommendation in a specific domain.The technique may not be language-specific.The method shows potential usability for e-learning in a specific domain.
recommender system digital publishing semantic relatedness
Mao Ye Zhi Tang Jianbo Xu Lifeng Jin
State Key Laboratory of Digital Publishing Technology(Peking University Founder Group Co.LTD.),Beiji State Key Laboratory of Digital Publishing Technology(Peking University Founder Group Co.LTD.),Beiji
国内会议
北京
英文
1-10
2014-11-01（万方平台首次上网日期，不代表论文的发表时间）