GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fast discovery of association rules
Advances in knowledge discovery and data mining
Conversational Case-Based Reasoning
Applied Intelligence
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Evaluating Preference-Based Feedback in Recommender Systems
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Evaluating example-based search tools
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Experiments in dynamic critiquing
Proceedings of the 10th international conference on Intelligent user interfaces
Integrating tradeoff support in product search tools for e-commerce sites
Proceedings of the 6th ACM conference on Electronic commerce
Generating Diverse Compound Critiques
Artificial Intelligence Review
A comparison of two compound critiquing systems
Proceedings of the 12th international conference on Intelligent user interfaces
On the evaluation of dynamic critiquing: a large-scale user study
AAAI'05 Proceedings of the 20th national 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
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
A visual interface for critiquing-based recommender systems
Proceedings of the 9th ACM conference on Electronic commerce
Reformulating Positive Table Constraints Using Functional Dependencies
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Case-studies on exploiting explicit customer requirements in recommender systems
User Modeling and User-Adapted Interaction
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)
Assessing regret-based preference elicitation with the UTPREF recommendation system
Proceedings of the 11th ACM conference on Electronic commerce
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Critiquing-based recommenders: survey and emerging trends
User Modeling and User-Adapted Interaction
Improving the performance of unit critiquing
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Inferring user utility for query revision recommendation
Proceedings of the 28th Annual ACM Symposium on Applied Computing
ReComment: towards critiquing-based recommendation with speech interaction
Proceedings of the 7th ACM conference on Recommender systems
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Conversational recommender systems are designed to help users to more efficiently navigate complex product spaces by alternatively making recommendations and inviting users' feedback. Compound critiquing techniques provide an efficient way for users to feed back their preferences (in terms of several simultaneous product attributes) when interfacing with conversational recommender systems. For example, in the laptop domain a user might wish to express a preference for a laptop that is "Cheaper, Lighter, with a Larger Screen". While recently a number of techniques for dynamically generating compound critiques have been proposed, to date there has been a lack of direct comparison of these approaches in a real-user study. In this paper we will compare two alternative approaches to the dynamic generation of compound critiques based on ideas from data mining and multi-attribute utility theory. We will demonstrate how both approaches support users to more efficiently navigate complex product spaces highlighting, in particular, the influence of product complexity and interface strategy on recommendation performance and user satisfaction.