Meta-Mender: A meta-rule based recommendation system for educational applications
Romero Zaldivar, Vicente Arturo
Burgos, Daniel (1)
Tipo de Ítem:Articulo Revista Indexada
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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