Jack S. Breese, David Heckerman, Carl M. Kadie Microsoft Research, Redmond WA, 98052-6399, USA firstname.lastname@example.org, email@example.com, firstname.lastname@example.orgDate Donated: November 30, 1998
J. Breese, D. Heckerman., C. Kadie _Empirical Analysis of Predictive Algorithms for Collaborative Filtering_ Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July, 1998.The train- and test set used in this paper are provided as 'anonymous-mswebtrain.dst' and 'anonymous-mswebtest.dst'
J. Breese, D. Heckerman., C. Kadie Empirical Analysis of Predictive Algorithms for Collaborative Filtering Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July, 1998.This paper presents a comparison of a number of memory-based (correlation and vector similarity techniques) as well as model-based (cluster models and Bayesian networks) methods. In terms of predictive accuracy, the results indicate that the authors' Bayesian network approach to collaborative filtering is the best performing approach on this dataset.