Abstract
A recommender system (RS) can infer constraints on the user utility function by observing the queries selected by a user among those it has suggested. Reasoning on these constraints it can avoid suggesting queries that retrieve products with an inferior utility, i.e., dominated queries. In this paper we propose a new efficient technique for the computation of dominated queries. It relies on the system's assumption that the number of possible profiles (utility functions) of the users it may interact with is finite. Under this assumption query suggestions can be efficiently computed and their number can be kept small. Moreover, we show that even if the system is not contemplating all the possible user profiles its performance is very close to the optimal one.