Evaluating compound critiquing recommenders: a real-user study
Proceedings of the 8th ACM conference on 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
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
Market-Oriented Grid and Utility Computing
Market-Oriented Grid and Utility Computing
Comparing Approaches to Preference Dominance for Conversational Recommenders
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 02
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
The lookahead principle for preference elicitation: experimental results
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering 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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A recommender system (RS) can infer constraints on the user utility function by observing the queries selected by a user among those it has suggested. Reasoning on these constraints it can avoid suggesting queries that retrieve products with an inferior utility, i.e., dominated queries. In this paper we propose a new efficient technique for the computation of dominated queries. It relies on the system's assumption that the number of possible profiles (utility functions) of the users it may interact with is finite. Under this assumption query suggestions can be efficiently computed and their number can be kept small. Moreover, we show that even if the system is not contemplating all the possible user profiles its performance is very close to the optimal one.