Difference between revisions of "2004 EvaluatingCollaborativeFilterin"

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=== Abstract ===
 
=== Abstract ===
  
[[Recommender system]]s have been evaluated in many, often incomparable, ways. </s>
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[[Recommender system]s have been evaluated in many, often incomparable, ways. </s>
 
In [[this article]], we review the [[key decisions in evaluating collaborative filtering recommender system]]s: the [[user task]]s being [[evaluated]], the types of [[analysi]]s and [[dataset]]s being used, the ways in which [[prediction quality]] is [[measured]], the [[evaluation]] of [[prediction]] [[attribute]]s other than [[quality]], and the [[user-based evaluation]] of the [[system]] as a whole. </s>
 
In [[this article]], we review the [[key decisions in evaluating collaborative filtering recommender system]]s: the [[user task]]s being [[evaluated]], the types of [[analysi]]s and [[dataset]]s being used, the ways in which [[prediction quality]] is [[measured]], the [[evaluation]] of [[prediction]] [[attribute]]s other than [[quality]], and the [[user-based evaluation]] of the [[system]] as a whole. </s>
 
In addition to reviewing the [[evaluation strategi]]es used by prior [[researcher]]s, [[we]] present [[empirical result]]s from the [[analysi]]s of various [[accuracy metric]]s on one [[content domain]] where all the [[tested metrics collapsed roughly]] into three [[equivalence classe]]s. </s>
 
In addition to reviewing the [[evaluation strategi]]es used by prior [[researcher]]s, [[we]] present [[empirical result]]s from the [[analysi]]s of various [[accuracy metric]]s on one [[content domain]] where all the [[tested metrics collapsed roughly]] into three [[equivalence classe]]s. </s>
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== References ==
 
== References ==
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* 1. Charu C. Aggarwal, Joel L. Wolf, Kun-Lung Wu, Philip S. Yu, Horting Hatches An Egg: A New Graph-theoretic Approach to Collaborative Filtering, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p.201-212, August 15-18, 1999, San Diego, California, USA [http://doi.acm.org/10.1145/312129.312230 doi:10.1145/312129.312230]
 
* 1. Charu C. Aggarwal, Joel L. Wolf, Kun-Lung Wu, Philip S. Yu, Horting Hatches An Egg: A New Graph-theoretic Approach to Collaborative Filtering, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p.201-212, August 15-18, 1999, San Diego, California, USA [http://doi.acm.org/10.1145/312129.312230 doi:10.1145/312129.312230]
 
* 2. Brian Amento, Loren Terveen, Will Hill, Deborah Hix, Robert Schulman, Experiments in Social Data Mining: The TopicShop System, ACM Transactions on Computer-Human Interaction (TOCHI), v.10 n.1, p.54-85, March 2003 [http://doi.acm.org/10.1145/606658.606661 doi:10.1145/606658.606661]
 
* 2. Brian Amento, Loren Terveen, Will Hill, Deborah Hix, Robert Schulman, Experiments in Social Data Mining: The TopicShop System, ACM Transactions on Computer-Human Interaction (TOCHI), v.10 n.1, p.54-85, March 2003 [http://doi.acm.org/10.1145/606658.606661 doi:10.1145/606658.606661]

Revision as of 05:36, 16 August 2019

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Abstract

[[Recommender system]s 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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  • 2. Brian Amento, Loren Terveen, Will Hill, Deborah Hix, Robert Schulman, Experiments in Social Data Mining: The TopicShop System, ACM Transactions on Computer-Human Interaction (TOCHI), v.10 n.1, p.54-85, March 2003 doi:10.1145/606658.606661
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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 +