A theory of diagnosis from first principles
Artificial Intelligence
The Wasabi Personal Shopper: a case-based recommender system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
ITR: A Case-Based Travel Advisory System
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Retrieval Failure and Recovery in Recommender Systems
Artificial Intelligence Review
Hybrid critiquing-based recommender systems
Proceedings of the 12th international conference on Intelligent user interfaces
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Supporting product selection with query editing recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Conversational recommenders with adaptive suggestions
Proceedings of the 2007 ACM conference on Recommender systems
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
Techniques for fast query relaxation in content-based recommender systems
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Conflict-directed relaxation of constraints in content-based recommender systems
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Knowledge-Based Systems
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“No matching product found” is an undesirable message for the user of an online product finder application or interactive recommender system. Query Relaxation is an approach to recovery from such retrieval failures and works by eliminating individual parts of the original query in order to find products that satisfy as many of the user's constraints as possible. In this paper, new techniques for the fast computation of “user-optimal” query relaxations are proposed. We show how the number of costly catalog queries can be minimized with the help of a query pre-processing approach, how we can compute relaxations that contain at least n items in the recommendation, and finally, how a recent conflict-detection algorithm can be applied for fast determination of preferred conflicts in interactive recovery scenarios.