Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Conversational Case-Based Reasoning
Applied Intelligence
Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
Increasing dialogue efficiency in case-based reasoning without loss of solution quality
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
minimizing dialog length in interactive case-based reasoning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Knowledge discovery from user preferences in conversational recommendation
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Completeness criteria for retrieval in recommender systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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In case-based reasoning (CBR) systems for product recommendation, the retrieval of acceptable products based on limited information is an important and challenging problem. As we show in this paper, basic retrieval strategies such as nearest neighbor are potentially unreliable when applied to incomplete queries. To address this issue, we present techniques for automating the discovery of recommendation rules that are provably reliable and non-conflicting while requiring minimal information for their application in a rule-based approach to the retrieval of recommended cases.