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

Autor: Vicente Arturo Romero Zaldivar, Daniel Burgos
Rok vydání: 2010
Předmět:
Zdroj: RecSysTEL@RecSys
ISSN: 1877-0509
DOI: 10.1016/j.procs.2010.08.014
Popis: 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.
Databáze: OpenAIRE