Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning and adaptivity in interactive recommender systems
Proceedings of the ninth international conference on Electronic commerce
Mining recommendations from the web
Proceedings of the 2008 ACM conference on Recommender systems
Controlled experiments on the web: survey and practical guide
Data Mining and Knowledge Discovery
A comparative user study on rating vs. personality quiz based preference elicitation methods
Proceedings of the 14th international conference on Intelligent user interfaces
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Recommendation systems are now widely used in many commercial applications. This tutorial focuses on the evaluation of such systems, from an application-oriented view. The tutorial recommends best practices, suggests a protocol for the evaluation process, and reviews a set of metrics that can be evaluated. The practices in this paper are motivated by similar procedures in nearby areas, such as machine learning, and information retrieval.