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A Semantic Recommender System for Learning Based on Encyclopedia of Digital Publication

  • Mao Ye
  • Lifeng Jin
  • Zhi Tang
  • Jianbo Xu
Part of the Communications in Computer and Information Science book series (CCIS, volume 435)

Abstract

Digital publication is a useful and authoritative resource for knowledge and learning. How to use the knowledge in digital publication resources so as to enhance learning is an interesting and important task. Most of the recommender systems use users’ preferences or history data for computation, which cannot solve the problems such as cold start, scarcity of history data or preferences data. A semantic recommender system is presented in this paper based on encyclopedic knowledge from digital publication resources, without considering history data or preferences data for learning the knowledge of a specific domain. Semantic relatedness is computed between concepts from the encyclopedia. The related concepts are recommended to users when one concept is reviewed. The method shows potential usability for domain-specific knowledge service.

Keywords

recommender system digital publication semantic relatedness 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mao Ye
    • 1
    • 2
    • 3
  • Lifeng Jin
    • 2
  • Zhi Tang
    • 1
    • 2
  • Jianbo Xu
    • 2
  1. 1.Peking UniversityBeijingChina
  2. 2.State Key Laboratory of Digital Publishing Technology (Peking University Founder Group Co. LTD.)BeijingChina
  3. 3.Postdoctoral Workstation of the Zhongguancun Haidian Science ParkBeijingChina

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