Conversational Case-Based Reasoning
Applied Intelligence
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
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
On the role of diversity in conversational recommender systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Knowledge-based navigation of complex information spaces
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning and adaptivity in interactive recommender systems
Proceedings of the ninth international conference on Electronic commerce
Mixed-Initiative Relaxation of Constraints in Critiquing Dialogues
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Critique graphs for catalogue navigation
Proceedings of the 2008 ACM conference on Recommender systems
Implications of psychological phenomenons for recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Case-studies on exploiting explicit customer requirements in recommender systems
User Modeling and User-Adapted Interaction
Improving recommender systems with adaptive conversational strategies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
The ins and outs of critiquing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Regret-based optimal recommendation sets in conversational recommender systems
Proceedings of the third ACM conference on Recommender systems
Experiments on the preference-based organization interface in recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Rapid development of knowledge-based conversational recommender applications with advisor suite
Journal of Web Engineering
A comparative study of compound critique generation in conversational recommender systems
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Experience-Based critiquing: reusing critiquing experiences to improve conversational recommendation
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Critiquing-based recommenders: survey and emerging trends
User Modeling and User-Adapted Interaction
Evaluating the effectiveness of explanations for recommender systems
User Modeling and User-Adapted Interaction
History-aware critiquing-based conversational recommendation
Proceedings of the 22nd international conference on World Wide Web companion
ReComment: towards critiquing-based recommendation with speech interaction
Proceedings of the 7th ACM conference on Recommender systems
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Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the user's feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a feature's value-space. For example, a user may request a cheaper product, thus critiquing the price feature. Usually, when critiquing is used in conversational recommender systems, there is little or no attempt to monitor successive critiques within a given recommendation session. In our experience this can lead to inefficiencies on the part of the recommender system, and confusion on the part of the user. In this paper we describe an approach to critiquing that attempts to consider a user's critiquing history, as well as their current critique, when making new recommendations. We provide experimental evidence to show that this has the potential to significantly improve recommendation efficiency.