Modeling Difficulty in Recommender Systems
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. Dening 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.