An Expressive Query Language for Product Recommender Systems
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
Experiments in dynamic critiquing
Proceedings of the 10th international conference on Intelligent user interfaces
Knowledge-Based Systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Mixed-Initiative Relaxation of Constraints in Critiquing Dialogues
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Critique graphs for catalogue navigation
Proceedings of the 2008 ACM conference on Recommender systems
ReComment: towards critiquing-based recommendation with speech interaction
Proceedings of the 7th ACM conference on Recommender systems
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Eliminating previously recommended products in critiquing limits the choices available to users when they attempt to navigate back to products they critiqued earlier in the dialogue (e.g., in search of cheaper alternatives). In the worst case, a user may find that the only product she is prepared to accept (e.g., having ruled out cheaper alternatives) has been eliminated. However, an equally serious problem if previous recommendations are not eliminated is that products that satisfy the user's requirements, if any, may be unreachable by any sequence of critiques. We present a new version of progressive critiquing that leaves open the option of repeating a previous recommendation while also addressing the unreachability problem. Our empirical results show that the approach is most effective when users refrain from over-critiquing attributes whose current values are acceptable.