Conversational Case-Based Reasoning
Applied Intelligence
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
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
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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
ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing
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
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Ontology-Aided product classification: a nearest neighbour approach
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
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Personalization actions that tailor the Web experience to a particular user are an integral component of recommender systems. Here, product knowledge – either hand-coded or “mined” – is used to guide users through the often overwhelming task of locating products they will like. Providing such intelligent user assistance and performing tasks on the user's behalf requires an understanding of their goals and preferences. As such, user feedback plays a critical role in the sense that it helps steer the search towards a “good” recommendation. Ideally, the system should be capable of effectively interpreting the feedback the user provides, and subsequently responding by presenting them with a “better” set of recommendations. In this paper we investigate a form of feedback known as critiquing. Although a large number of recommenders are well suited to this form of feedback, we argue that on its own it can lead to inefficient recommendation dialogs. As a solution we propose a novel recommendation technique that has the ability to dramatically improve the utility of critiquing.