Meta-rules: Improving Adaptation in Recommendation Systems

Recommendation Systems are central in current applications to help the user find useful information spread in large amounts of post, videos or social networks. Most Recommendation Systems are more effective when huge amounts of user data are available in order to calculate similarities between users. Educational applications are not popular enough in order to generate large amount of data. In this context, rule-based Recommendation Systems are a better solution. Rules are in most cases written a priori by domain experts; they can offer good recommendations with even no application of usage information. However large rule-sets are hard to maintain , reengineer and adapt to user goals and preferences. Metarules , rules that generate rules, can generalize a rule-set providing bases for adaptation, reengineering and on the fly generation. In this paper, the authors expose the benefits of metarules implemented as part of a metarule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach in rule-based Recommendation Systems.