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A Model for a Collaborative Recommender System for Multimedia Learning Material

  • Nelson Baloian
  • Patricio Galdames
  • César A. Collazos
  • Luis A. Guerrero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3198)

Abstract

In a cluster of many servers containing heterogeneous multimedia learning material and serving users with different backgrounds (e.g. language, interests, previous knowledge, hardware and connectivity) it may be difficult for the learners to find a piece of material which fit their needs. This is the case of the COLDEX project. Recommender systems have been used to help people sift through all the available information to find that most valuable to them. We propose a recommender system, which suggest multimedia learning material based on the learner’s background preferences as well as the available hardware and software that he/she has.

Keywords

Bayesian Network Recommender System User Profile Learning Material Collaborative Filter 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Nelson Baloian
    • 1
  • Patricio Galdames
    • 1
    • 2
  • César A. Collazos
    • 3
  • Luis A. Guerrero
    • 1
  1. 1.Department of Computer ScienceUniversidad de ChileSantiagoChile
  2. 2.Department of Electrical EngineeringUniversidad de ChileSantiagoChile
  3. 3.Department of SystemsUniversidad del Cauca, FIET-Sector TulcanPopayánColombia

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