User Modeling in Human–Computer Interaction
User Modeling and User-Adapted Interaction
simVar: A Similarity-Influenced Question Selection Criterion for e-Sales Dialogs
Artificial Intelligence Review
IEEE Transactions on Knowledge and Data Engineering
Retrieval Failure and Recovery in Recommender Systems
Artificial Intelligence Review
Increasing user decision accuracy using suggestions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Case-based recommender systems
The Knowledge Engineering Review
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Donation dashboard: a recommender system for donation portfolios
Proceedings of the third ACM conference on Recommender systems
Fast computation of query relaxations for knowledge-based recommenders
AI Communications
Improving Decision Quality Through Preference Relaxation
Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade
Interactive recommendations in social endorsement networks
Proceedings of the fourth ACM conference on Recommender systems
Intelligent product search with soft-boundary preference relaxation
Expert Systems with Applications: An International Journal
Inferring user utility for query revision recommendation
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Acquiring user profiles from implicit feedback in a conversational recommender system
Proceedings of the 7th ACM conference on Recommender systems
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Consider a conversational product recommender system in which a user repeatedly edits and resubmits a query until she finds a product that she wants. We show how an advisor can: observe the user's actions; infer constraints on the user's utility function and add them to a user model; use the constraints to deduce which queries the user is likely to try next; and advise the user to avoid those that are unsatisfiable. We call this information recommendation. We give a detailed formulation of information recommendation for the case of products that are described by a set of Boolean features. Our experimental results show that if the user is given advice, the number of queries she needs to try before finding the product of highest utility is greatly reduced. We also show that an advisor that confines its advice to queries that the user model predicts are likely to be tried next will give shorter advice than one whose advice is unconstrained by the user model.