Enriching buyers' experiences: the SmartClient approach
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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
Generating Diverse Compound Critiques
Artificial Intelligence Review
Knowledge-Based Systems
On the role of diversity in conversational recommender systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Hybrid critiquing-based recommender systems
Proceedings of the 12th international conference on Intelligent user interfaces
A comparison of two compound critiquing systems
Proceedings of the 12th international conference on Intelligent user interfaces
Evaluating compound critiquing recommenders: a real-user study
Proceedings of the 8th ACM conference on Electronic commerce
A visual interface for critiquing-based recommender systems
Proceedings of the 9th ACM conference on Electronic commerce
Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems
UM '07 Proceedings of the 11th international conference on User Modeling
Critiquing recommenders for public taste products
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)
Proceedings of the 16th international conference on Intelligent user interfaces
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
Improving the performance of unit critiquing
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
The Tag Genome: Encoding Community Knowledge to Support Novel Interaction
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Common Sense for Interactive Systems
User-oriented product search based on consumer values and lifestyles
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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
Acquiring user profiles from implicit feedback in a conversational recommender system
Proceedings of the 7th ACM conference on Recommender systems
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Critiquing techniques provide an easy way for users to feedback their preferences over one or several attributes of the products in a conversational recommender system. While unit critiques only allow users to critique one attribute of the products each time, a well-generated set of compound critiques enables users to input their preferences on several attributes at the same time, and can potentially shorten the interaction cycles in finding the target products. As a result, the dynamic generation of compound critiques is a critical issue for designing the critique-based conversational recommender systems. In earlier research the Apriori algorithm has been adopted to generate compound critiques from the given data set. In this paper we propose an alternative approach for generating compound critiques based on the multi-attribute utility theory (MAUT). Our approach automatically updates the weights of the product attributes as the result of the interactive critiquing process. This modification of weights is then used to determine the compound critiques according to those products with the highest utility values. Our experiments show that the compound critiques generated by this approach are more efficient in helping users find their target products than those generated by the Apriori algorithm.