Constraint-based Recommender System for Commodity Realization

In this paper, we suggest a novel recommender system where a set of appropriate propositions is formed by measuring how user query features are close to space of all possible propositions. The system is for e-traders selling commodities. A commodity has hierarchical-structure properties which are mapped to the respective numerical scales. The scales are normalized so that a query from a potential customer and any possible proposition from the e-trader is a multidimensional point of a nonnegative unit hypercube put on the coordinate origin. The user can weight levels. The distance between the query and propositions are measured by the respective metric in the Euclidean arithmetic space. The best proposition is defined by the shortest distance. Top N propositions are defined by N shortest distances. The system does not depend on any user experience, nor on the e-trader tendency to impose one's preferences on the customer.