Modeling the Collaborative User Groups and Their Effectiveness for the Contents Recommender

  • Saim Shin
  • Sung-Joo Park
  • Da-Hee Kim
  • Se-Jin Jang
  • Soek-Pil Lee
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 214)


In this paper, we model the contents recommender which applies the collaborative filtering and the vector comparison techniques. The system mined the users’ usage history about consuming contents, suggested the user favorable contents. We constructed the usage history data set about 49 users for showing the effectiveness of the proposed algorithms, the results were showed that the collaborative filtering technologies are helpful to resolve the data sparseness problems in the contents recommender.


Contents recommender Collaborative filtering 



This work was supported by the Ministry of Knowledge Economy (MKE) grant funded by Korea government (No. 10037244)


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Saim Shin
    • 1
  • Sung-Joo Park
    • 1
  • Da-Hee Kim
    • 1
  • Se-Jin Jang
    • 1
  • Soek-Pil Lee
    • 2
  1. 1.Digital Media Research CenterKorea Electronics Technology InstituteSeoulKorea
  2. 2.Sang-Myung UniversityKorea Electronics Technology InstituteSeoulKorea

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