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

dc.contributor.authorRomero Zaldivar, Vicente Arturo
dc.contributor.authorBurgos, Daniel
dc.date2010
dc.date.accessioned2017-12-21T16:23:38Z
dc.date.available2017-12-21T16:23:38Z
dc.identifier.issn1877-0509
dc.identifier.urihttps://reunir.unir.net/handle/123456789/6093
dc.description.abstractRecommenders 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.es_ES
dc.language.isoenges_ES
dc.publisherProcedia Computer Sciencees_ES
dc.relation.ispartofseries;vol. 1, nº 2
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S1877050910003273es_ES
dc.rightsopenAccesses_ES
dc.subjectrule-based recommendation systemses_ES
dc.subjectpersonalizationes_ES
dc.subjectadaptationes_ES
dc.subjectmeta-rulees_ES
dc.subjectrule generationes_ES
dc.subjectScopuses_ES
dc.titleMeta-Mender: A meta-rule based recommendation system for educational applicationses_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1016/j.procs.2010.08.014


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