Conversational Case-Based Reasoning
Applied Intelligence
Artificial Intelligence Review
Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
On the evaluation of dynamic critiquing: a large-scale user study
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Increasing dialogue efficiency in case-based reasoning without loss of solution quality
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Automating the discovery of recommendation knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Knowledge-based navigation of complex information spaces
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Flexible and dynamic compromises for effective recommendations
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Knowledge discovery for personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems to increase efficiency and effectiveness. Conversational recommender systems guide users through a product space, alternatively making product suggestions and eliciting user feedback. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over recommendations, instead of providing specific value preferences. For example, a user might ask for a ‘less expensive’ vacation in a travel recommender; thus ‘less expensive’ is a critique over the price feature. The expectation is that on each cycle, the system discovers more about the user’s soft product preferences from minimal information input. In this paper we describe three different strategies for knowledge discovery from user preferences that improve recommendation efficiency in a conversational system using critiquing. Moreover, we will demonstrate that while the strategies work well separately, their combined effort has the potential to considerably increase recommendation efficiency even further.