Meta-rule based recommender systems for educational applications
Date
2011
Authors
Romero Zaldivar, V.A.
Burgos, D.
Pardo, A.
Editors
Santos, O.C.
Boticario, J.G.
Boticario, J.G.
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Book chapter
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Source details - Title: Educational recommender systems and technologies, 2011 / Santos, O.C., Boticario, J.G. (ed./s), Ch.9, pp.211-231
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Abstract
Recommendation 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.
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Copyright 2011 IGI Global