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Information 2015, 6(3), 443-453;

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

1,2,*, 1, 1 and 1
State Key Laboratory of Digital Publishing Technology (Peking University Founder Group Co. Ltd.), Beijing 100089, China
Postdoctoral Workstation of the Zhongguancun Haidian Science Park, Beijing 100089, China
Author to whom correspondence should be addressed.
Academic Editors: Lawrence J. Henschen and Ning Li
Received: 28 December 2014 / Accepted: 27 July 2015 / Published: 4 August 2015
PDF [738 KB, uploaded 5 August 2015]


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. View Full-Text
Keywords: recommender system; digital publishing; semantic relatedness recommender system; digital publishing; semantic relatedness

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ye, M.; Tang, Z.; Xu, J.; Jin, L. Recommender System for E-Learning Based on Semantic Relatedness of Concepts. Information 2015, 6, 443-453.

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