Our customer preference model is based on aggregation of partly linear relaxations of value filters often used in e-commerce applications. Relaxation is motivated by the Analytic Hierarchy Processing method and combining fuzzy information in web accessible databases. In low dimensions our method is well suited also for data visualization. The process of translating models (user behavior) to programs (learned recommendation) is formalized by Challenge-Response Framework ChRF. ChRF resembles remote process call and reduction in combinatorial search problems. In our case, the model is automatically translated to a program using spatial database features. This enables us to define new metrics with visual motivation. We extend the conference paper with inductive ChRF, new representation of user and an additional method and metric. We provide experiments with synthetic data (items) and users.