Meta-Rule Based Recommender Systems for Educational Applications

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.

Towards Learning Object Recommendations Based on Teachers' ICT Competence Profiles

“…content-based recommenders (CB), collaborative filtering (CF) recommenders and hybrid recommenders (H) [3]. Other methods, such as rule-based (RB) recommendations [4] are less popular.…”