Recommender System for E-Learning Based on Semantic Relatedness of Concepts

Information

Mao Ye²  Zhi Tang²  Jianbo Xu²  Lifeng Jin²  Lawrence J. Henschen¹ 

[1] State Key Laboratory of Digital Publishing Technology (Peking University Founder Group Co. Ltd.), Beijing 100089, China;;State Key Laboratory of Digital Publishing Technology (Peking University Founder Group Co. Ltd.), Beijing 100089, China; E-Mails:

关键词: recommender system;    digital publishing;    semantic relatedness;   

DOI  :  10.3390/info6030443

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.