Multiagent Based Recommendation System Model for Indexing and Retrieving Learning Objects

  • Ronaldo Lima Rocha Campos
  • Rafaela Lunardi Comarella
  • Ricardo Azambuja Silveira
Part of the Communications in Computer and Information Science book series (CCIS, volume 365)


This paper proposes a multiagent system application model for indexing, retrieving and recommending learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata (data about data) standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the relevance of the results we propose an information retrieval model based on a multiagent system approach and an ontological model to describe the covered knowledge domain.


multiagent system recommender system ontology learn object information retrieval 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ronaldo Lima Rocha Campos
    • 1
  • Rafaela Lunardi Comarella
    • 1
  • Ricardo Azambuja Silveira
    • 1
  1. 1.Federal University of Santa Catarina (UFSC)FlorianópolisBrazil

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