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Research on Personalized Knowledge Service System in Community E-Learning

  • Yan-wen Wu
  • Qi Luo
  • Yu-jun Liu
  • Ying Yu
  • Zhao-hua Zhang
  • Yan Cao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3942)

Abstract

To meet the needs of education in the learning community, personalized knowledge service system based on community E-learning (PKSSCE) was proposed and realized. The structure of system, workflow and key technologies of realizing feature selection module, user interest module, personalized teaching resources filtering module were introduced in the paper. After the test use of the system by some certain communities, we found that the system could improve the residents initiative participation in the community education and training. Thus, we thought it might be a practical solution to realize self-learning and self-promotion in the life long education age.

Keywords

Teaching Resource User Interest Edge Service Association Rule Mining Algorithm Interest Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Qi, L.: Research on Application of Association Rule Mining Algorithm in Learning Community. In: CAAI-11, Wuhan, pp. 1458–1462 (2005)Google Scholar
  2. 2.
    Yan-wen, W., Zhonghong, W.: Knowledge Adaptive Presentation Strategy in E-Learning. In: Second International Conference on Knowledge Economy and Development of Science and Technology (KEST 2004), Beijing, pp. 6–9 (2004)Google Scholar
  3. 3.
    Lawrence, R.D., Almasi, G.S., Kotlyar, V., et al.: Personalization of Supermarket Product Reommendations. Special Issue of the International Journal Data Mining and Knowledge Discovery 5, 11–32 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Xin, N.: Take about the Digital Individualized Information Service of Library. Information Science Journal 23, 1–5 (2005)Google Scholar
  5. 5.
    Robertson, S., Hull, D.A.: The TREC-9 filtering track final report. In: Proceedings of the 9th Text Retrieval Conference (TREC-9), Gaithersburg, pp. 25–40 (2001)Google Scholar
  6. 6.
    Dun, L., Yuanda, C.: A New Weighted Text Filtering Method. In: International Confer-ence on Natural Language Processing and Knowledge Engineering(IEEE NLP-KE 2005), wuhan, pp. 695–698 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yan-wen Wu
    • 1
  • Qi Luo
    • 1
  • Yu-jun Liu
    • 2
  • Ying Yu
    • 1
  • Zhao-hua Zhang
    • 1
  • Yan Cao
    • 1
  1. 1.Department of Information TechnologyCentral China Normal UniversityWuhanChina
  2. 2.Department of Educational TechnologyZhejiang University of TechnologyHangzhouChina

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