A Heuristic Approach for New-Item Cold Start Problem in Recommendation of Micro Open Education Resources

  • Geng SunEmail author
  • Tingru Cui
  • Dongming Xu
  • Jun Shen
  • Shiping Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10858)


The recommendation of micro Open Education Resources (OERs) suffers from the new-item cold start problem because little is known about the continuously published micro OERs. This paper provides a heuristic approach to inserting newly published micro OERs into established learning paths, to enhance the possibilities of new items to be discovered and appear in the recommendation lists. It considers the accumulation and attenuation of user interests and conform with the demand of fast response in online computation. Performance of this approach has been proved by empirical studies.


Cold start Open Education Resources Adaptive micro learning Heuristic recommendation Learning path 



This research has been conducted with the support of the Australian Research Council Discovery Project, DP180101051.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Geng Sun
    • 1
    Email author
  • Tingru Cui
    • 1
  • Dongming Xu
    • 2
  • Jun Shen
    • 1
  • Shiping Chen
    • 3
  1. 1.School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia
  2. 2.UQ Business SchoolThe University of QueenslandBrisbaneAustralia
  3. 3.CSIRO Data61SydneyAustralia

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