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    Meta-Mender: A meta-rule based recommendation system for educational applications

    Autor: 
    Romero Zaldivar, Vicente Arturo
    ;
    Burgos, Daniel (1)
    Fecha: 
    2010
    Palabra clave: 
    rule-based recommendation systems; personalization; adaptation; meta-rule; rule generation; Scopus
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/6093
    DOI: 
    https://doi.org/10.1016/j.procs.2010.08.014
    Dirección web: 
    http://www.sciencedirect.com/science/article/pii/S1877050910003273
    Open Access
    Resumen:
    Recommenders are central in current applications to help the user find useful information spread in large amounts of data. Most Recommenders are more effective when huge amounts of user data are available in order to calculate user similarities. In general, educational applications are not popular enough in order to generate large amount of data. In this context, rule-based Recommenders are a better solution. Meta-rules can generalize a rule-set, providing bases for adaptation. The authors present a meta-rule based Recommender as an effective solution to provide a personalized recommendation to the learner, which is a new approach in rule-based Recommender Systems.
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