Difference between revisions of "2004 EvaluatingCollaborativeFilterin"

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* 4. Ricardo A. Baeza-Yates, Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
 
* 4. Ricardo A. Baeza-Yates, Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
 
* 5. Bailey, B. P., Gurak, L. J., and Konstan, J. A. 2001. An Examination of Trust Production in Computer-mediated Exchange. In: Proceedings of the 7th Conference on Human Factors and the Web (July).]]
 
* 5. Bailey, B. P., Gurak, L. J., and Konstan, J. A. 2001. An Examination of Trust Production in Computer-mediated Exchange. In: Proceedings of the 7th Conference on Human Factors and the Web (July).]]
* 6. Marko Balabanović, Yoav Shoham, Fab: Content-based, Collaborative Recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [http://doi.acm.org/10.1145/245108.245124 doi:10.1145/245108.245124]
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* 6. Marko Balabanović, [[Yoav Shoham]], Fab: Content-based, Collaborative Recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [http://doi.acm.org/10.1145/245108.245124 doi:10.1145/245108.245124]
 
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* 7. Chumki Basu, Haym Hirsh, William Cohen, Recommendation As Classification: Using Social and Content-based Information in Recommendation, Proceedings of the Fifteenth National/tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, p.714-720, July 1998, Madison, Wisconsin, USA
 
* 8. Daniel Billsus, Michael J. Pazzani, Learning Collaborative Information Filters, Proceedings of the Fifteenth International Conference on Machine Learning, p.46-54, July 24-27, 1998
 
* 8. Daniel Billsus, Michael J. Pazzani, Learning Collaborative Information Filters, Proceedings of the Fifteenth International Conference on Machine Learning, p.46-54, July 24-27, 1998

Revision as of 14:40, 13 August 2019

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Abstract

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 EvaluatingCollaborativeFilterinJonathan L. Herlocker
Joseph A. Konstan
Loren G. Terveen
John T. Riedl
Evaluating Collaborative Filtering Recommender Systems10.1145/963770.9637722004
AuthorJonathan L. Herlocker +, Joseph A. Konstan +, Loren G. Terveen + and John T. Riedl +
doi10.1145/963770.963772 +
titleEvaluating Collaborative Filtering Recommender Systems +
year2004 +