Anonymous web data from www.microsoft.com
Task Type
Classification, Collaborative Filtering 数据挖掘研究院
Sources
Donor
Jack S. Breese, David Heckerman, Carl M. Kadie Microsoft Research, Redmond WA, 98052-6399, USA breese@microsoft.com, heckerma@microsoft.com, carlk@microsoft.com 数据挖掘研究院
Date Donated: November 30, 1998
Problem Description
Analysis Task
Predict the areas of www.microsoft.com that a user visited based on data on what other areas he or she visited. 数据挖掘研究院
Evaluation Criteria and Constraints
Important solution characteristics are: predictive accuracy, learning time, and speed of predictions.
Preprocessing and Modifications
No additional preprocessing to the data was done. 数据挖掘研究院
Other Relevant Information
Experimental procedures are described in: 数据挖掘研究院
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. 数据挖掘研究院
The train- and test set used in this paper are provided as ′anonymous-mswebtrain.dst′ and ′anonymous-mswebtest.dst′
Results
Results for this dataset are reported in:
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. 数据挖掘研究院
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. 数据挖掘研究院
References and Further Information
Results on this dataset were expanded as Microsoft Research Technical Report MSR-TR-98-12. 数据挖掘研究院
