Fast discovery of association rules
Advances in knowledge discovery and data mining
Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Designing example-critiquing interaction
Proceedings of the 9th international conference on Intelligent user interfaces
Evaluating example-based search tools
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
On the evaluation of dynamic critiquing: a large-scale user study
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
minimizing dialog length in interactive case-based reasoning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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
Hybrid critiquing-based recommender systems
Proceedings of the 12th international conference on Intelligent user interfaces
Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Preference-based search with adaptive recommendations
AI Communications - Recommender Systems
Interaction design guidelines on critiquing-based recommender systems
User Modeling and User-Adapted Interaction
Evaluating preference-based search tools: a tale of two approaches
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
Experiments on the preference-based organization interface in recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Hybrid web recommender systems
The adaptive web
Critiquing-based recommenders: survey and emerging trends
User Modeling and User-Adapted Interaction
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Feature critiquing has emerged as an important feedback strategy for conversational recommender systems as it offers a useful balance between user effort and recommendation efficiency. Dynamic critiquing has recently been presented as an extension to conventional (single-feature) critiquing that supports the simultaneous critiquing of multiple features. To date, dynamic critiquing has been evaluated through a variety of artificial user trials to demonstrate its potential advantages, when it comes to improving recommendation efficiency and quality. However these advantages have never been verified through any large-scale user trial. The contribution of this paper is that we present the results of such an evaluation, which confirms the advantages of dynamic critiquing in a realistic online, e-commerce setting. Furthermore we investigate the impact of implicitly maintaining session specific user models to influence the selection of compound critiques. These models are incrementally constructed as the user critiques example recommendations from cycle to cycle. Our live-user evaluation also enabled us to analyse how real users interact with the compound critiques that are produced in this way. The results demonstrate that our incremental critiquing approach has the capability of generating more relevant critique options, and that users frequently recognise the benefits associated with using these as feedback options, leading to significantly shorter recommendation sessions.