  We propose a new approach for building recommender systems by adapting
surrogate-assisted interactive genetic algorithms. A pool of user-evaluated
items is used to construct an approximative model which serves as a surrogate
fitness function in a genetic algorithm for optimizing new suggestions. The
surrogate is used to recommend new items to the user, which are then evaluated
according to the user's liking and subsequently removed from the search space.
By updating the surrogate model after new recommendations have been evaluated
by the user, we enable the model itself to evolve towards the user's
preferences. In order to precisely evaluate the performance of that approach,
the human's subjective evaluation is replaced by common continuous objective
benchmark functions for evolutionary algorithms. The system's performance is
compared to a conventional genetic algorithm and random search. We show that
given a very limited amount of allowed evaluations on the true objective, our
approach outperforms these baseline methods.
