Exploring more realistic evaluation measures for collaborative filtering

Authors:
Giuseppe Carenini;Rita Sharma
Affiliations:
Computer Science Dept., University of British Columbia, Vancouver, BC, Canada;Computer Science Dept., University of British Columbia, Vancouver, BC, Canada
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

Citing 7
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Abstract

Collaborative filtering is a popular technique for recommending items to people. Several methods for collaborative filtering have been proposed in the literature and the quality of their predictions compared in empirical studies, In this paper, we argue that the measures of quality used in these studies are based on rather simple assumptions. We propose and apply additional measures for comparing the effectiveness of collaborative filtering methods which are grounded in decision-theory.