Abstrakt
A real problem use-case represents a challenge. This is usually transformed (reduced) to a model.
We expect the model to give a response/solution which is (at least in a degree) acceptable/meets the challenge. Moreover this challenge-response understanding has two levels - both the real world situation and model situation contains challenge side (input, query, problem...) and the response side (output, answer, solution...).
We present a formal model of ChRF-Challenge-Response Framework inspired by our previous work on Galois-Tukey connections. Nevertheless, real world reduction to models needs some adaptation of this formal model.
In this paper we introduce several examples extending ChRF. We illustrate this using several practical situations mainly in the area of recommender systems.
Data of the model situations are motivated by Fagin-Lotem-Naor's data model with attribute preferences and multicriterial aggregation. In this realm we review our previous work on preferential interpretation of fuzzy sets; implicit behavior in/and online/offline evaluation of recommender systems.
We finish with smart extensions of industrial processes. We propose a synthesis of these and formulate some open problems.