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Hybrid recommender systems are capable of providing better rec-ommendations than non-hybrid ones. Our approach to hybrid recommenders is the use of prediction strategies that determine which prediction technique(s) should be used at the moment an actual prediction is required. In this paper, we determine whether case-based reasoning can provide more accurate prediction strategies than rule-based predictions strategies created manually by experts. Experiments show that case-based reasoning can indeed be used to create pre-diction strategies; it can even increase the accuracy of the recommender in sys-tems where the accuracy of the used prediction techniques is highly spread.

Hybrid recommender systems are capable of providing better rec-ommendations than non-hybrid ones. Our approach to hybrid recommenders is the use of prediction strategies that determine which prediction technique(s) should be used at the moment an actual prediction is required. In this paper, we determine whether case-based reasoning can provide more accurate prediction strategies than rule-based predictions strategies created manually by experts. Experiments show that case-based reasoning can indeed be used to create pre-diction strategies; it can even increase the accuracy of the recommender in sys-tems where the accuracy of the used prediction techniques is highly spread.