Meta-Mender: A meta-rule based recommendation system for educational applications
Autor: | Vicente Arturo Romero Zaldivar, Daniel Burgos |
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Rok vydání: | 2010 |
Předmět: |
Information retrieval
Personalization Computer science Context (language use) Rule-based system Rule-based recommendation systems Recommender system Ontology (information science) Rule generation Meta-rule Order (business) Ontology General Earth and Planetary Sciences Adaptive learning Adaptation Adaptation (computer science) General Environmental Science |
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 |
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