Evaluating the Intrusion cost of recommending in Recommender Systems

Abstract. Recommender systems suggest items, guiding the user in a personalized way in a large space of possible options. To accomplish this task, they should try to bother users as less as possible, but each recommendation occupies expensive room in the always small user interface. Unfortunately, current evaluation of recommender systems do not have into account this cost. This work presents some new measures that have into account this intrusion cost while recommending. Some experiments are performed to compare our approach with traditional ones.