Abstract

Recommender systems have frequently been evaluated with respect to their average performance for all users. However, optimizing such recommender systems regarding those evaluation measures might provide worse results for a subset of users. Dening a difficulty measure allows us to evaluate and optimize recommender systems in a personalized fashion. We introduce an experimental setup to evaluate the eligibility of such a difficulty score. We formulate the hypothesis that provided a difficulty score recommender systems can be optimized regarding costs and performance simultaneously.