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dc.contributor.authorRomero Zaldivar, Vicente Arturo
dc.contributor.authorBurgos, Daniel
dc.contributor.authorPardo, Abelardo
dc.description.abstractRecommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are available. Educational applications are not popular enough to generate large amount of data. In this context, rule-based Recommendation Systems seem a better solution. Rules can offer specific recommendations with even no usage information. However, large rule-sets are hard to maintain, reengineer, and adapt to user preferences. Meta-rules can generalize a rule-set which provides bases for adaptation. In this chapter, the authors present the benefits of meta-rules, implemented as part of Meta-Mender, a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach to Recommendation Systems.es_ES
dc.publisherIGI Globales_ES
dc.publisherEducational Recommender Systems and Technologies: Practices and Challengeses_ES
dc.titleMeta-rule based recommender systems for educational applicationses_ES
dc.title.alternativeEducational Recommender Systems and Technologies: Practices and Challengeses_ES

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