Аннотация:
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.
