Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Conversational Case-Based Reasoning
Applied Intelligence
Machine Learning
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Simplifying decision trees: A survey
The Knowledge Engineering Review
minimizing dialog length in interactive case-based reasoning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Explanation in Recommender Systems
Artificial Intelligence Review
Conversational Case-Based Reasoning in Self-healing and Recovery
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Automating the discovery of recommendation knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Human-machine learning for intelligent aircraft systems
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
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
Supporting conversation variability in COBBER using causal loops
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Completeness criteria for retrieval in recommender systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Learning an ontology for visual tasks
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
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Increasing dialogue efficiency in case-based reasoning (CBR) must be balanced against the risk of commitment to a sub-optimal solution. Focusing on incremental query elicitation in recommender systems, we examine the limitations of naive strategies such as terminating the dialogue when the similarity of any case reaches a predefined threshold. We also identify necessary and sufficient conditions for recommendation dialogues to be terminated without loss of solution quality. Finally, we evaluate a number of attribute-selection strategies in terms of dialogue efficiency given the requirement that there must be no loss of solution quality.