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.
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References
Burke, R.: User Modeling and User-Adapted Interaction (2002)
Billsus, D., Pazzani, M.J.: In: Proc. 15th International Conf. on Machine Learning, pp. 46–54. Morgan Kaufmann, San Francisco (1998)
Pazzani, M., Muramatsu, J., Billsus, D.: In: Proceedings of the National Conference on Artificial Intelligence, vol. 1, pp. 54–61 (1996)
Shardanand, U., Maes, P.: In: CHI 1995: Proceedings of the Conference of Human Factors in Computing Systems. ACM Press, New York (1995)
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© 2005 Springer-Verlag Berlin Heidelberg
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Hernandez-del-Olmo, F., Gaudioso, E., Boticario, J.G. (2005). Evaluating the Intrusion Cost of Recommending in Recommender Systems. In: Ardissono, L., Brna, P., Mitrovic, A. (eds) User Modeling 2005. UM 2005. Lecture Notes in Computer Science(), vol 3538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527886_45
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DOI: https://doi.org/10.1007/11527886_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27885-6
Online ISBN: 978-3-540-31878-1
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