C4.5: programs for machine learning
C4.5: programs for machine learning
Case-based reasoning with confidence
Case-based reasoning with confidence
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
An architecture for more realistic conversational systems
Proceedings of the 6th international conference on Intelligent user interfaces
Conversational Case-Based Reasoning
Applied Intelligence
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Personalized Conversational Case-Based Recommendation
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
A Dynamic Approach to Reducing Dialog in On-Line Decision Guides
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Case-Based Reasoning with Confidence
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Helping a CBR Program Know What It Knows
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Benefits of Case-Based Reasoning in Color Matching
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Designing example-critiquing interaction
Proceedings of the 9th international conference on Intelligent user interfaces
minimizing dialog length in interactive case-based reasoning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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The ability of a CBR system to evaluate its own confidence in a proposed solution is likely to have an important impact on its problem solving and reasoning ability; if nothing else it allows a system to respond with “I don't know” instead of suggesting poor solutions. This ability is especially important in interactive CBR recommender systems because to be successful these systems must build trust with their users. This often means helping users to understand the reasons behind a particular recommendation, and presenting them with explanations, and confidence information is an important way to achieve this. In this paper we propose an explicit model of confidence for conversational recommendation systems. We explain how confidence can be evaluated at the feature-level, during each cycle of a recommendation session, and how this can be effectively communicated to the user. In turn, we also show how case-level confidence can be usefully incorporated into the recommendation logic to guide the recommender in the direction of more confident suggestions.