Methods for selecting the best system
WSC '91 Proceedings of the 23rd conference on Winter simulation
Sensitivity analysis in ranking and selection for multiple performance measures
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A Multiple Attribute Utility Theory Approach to Ranking and Selection
Management Science
Indifference zone selection procedures: inferences from indifference-zone selection procedures
Proceedings of the 35th conference on Winter simulation: driving innovation
An intelligent fuzzy-based recommendation system for consumer electronic products
Expert Systems with Applications: An International Journal
Multivariate preference models and decision making with the MAUT machine
UM'03 Proceedings of the 9th international conference on User modeling
A preference-based recommender system
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
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Identifying products which are appropriate to fit the consumer's preferences has become a crucial process in electronic commerce. Many recommender systems have been examined to ensure adequate product recommendations. Some of these systems try to estimate the utility each product may provide to a particular consumer. Since selecting the best product is mostly not possible, the question of how many and which products are recommendable is imperative. In this paper we present an approach to distinguish between recommendable and not recommendable products based on their expected utility. Our approach uses signal detection theory to facilitate a theory-driven procedure for the distinction.