Predictability of Off-line to On-line Recommender Measures via Scaled Fuzzy Implicators

This paper introduces fuzzy Challenge Response Framework, designed to understand the relationship between the model of a real-world situation and some real observations, based on scaled fuzzy Implicators between them. This general framework is applied to a particular case in recommender systems: the prediction of on-line performance given off-line evaluation results. We perform an empirical evaluation with real data from a Czech travel agency, comparing different recommender algorithms, different metrics for on-line and offline evaluations, and different implication operators.