Evaluating the intrusion cost of recommending in recommender systems

Authors:
Felix Hernandez-del-Olmo;Elena Gaudioso;Jesus G. Boticario
Affiliations:
Dpto. de Inteligencia Artificial, Universidad Nacional de Educacion a Distancia, Madrid, Spain;Dpto. de Inteligencia Artificial, Universidad Nacional de Educacion a Distancia, Madrid, Spain;Dpto. de Inteligencia Artificial, Universidad Nacional de Educacion a Distancia, Madrid, Spain
Venue:
UM'05 Proceedings of the 10th international conference on User Modeling
Year:
2005

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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.