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Fuzzy queries in multimedia database systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Enriching buyers' experiences: the SmartClient approach
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Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
Hybrid Recommender Systems: Survey and Experiments
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The FindMe Approach to Assisted Browsing
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Making Rational Decisions Using Adaptive Utility Elicitation
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Predicting Future User Actions by Observing Unmodified Applications
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A POMDP formulation of preference elicitation problems
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Evaluating example-based search tools
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Effective Interaction Principles for Online Product Search Environments
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Experiments in dynamic critiquing
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Increasing user decision accuracy using suggestions
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Foundations of preferences in database systems
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Evaluating preference-based search tools: a tale of two approaches
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Preference-based search using example-critiquing with suggestions
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ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing
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The lookahead principle for preference elicitation: experimental results
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
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Donation dashboard: a recommender system for donation portfolios
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Proceedings of the third ACM conference on Recommender systems
Fast computation of query relaxations for knowledge-based recommenders
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The effect of preference elicitation methods on the user experience of a recommender system
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Improving Decision Quality Through Preference Relaxation
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Interactive recommendations in social endorsement networks
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Who is talking about what: social map-based recommendation for content-centric social websites
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Who is Doing What and When: Social Map-Based Recommendation for Content-Centric Social Web Sites
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User Modeling and User-Adapted Interaction
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We consider a conversational recommender system based on example-critiquing where some recommendations are suggestions aimed at stimulating preference expression to acquire an accurate preference model. User studies show that suggestions are particularly effective when they present additional opportunities to the user according to the look-ahead principle [32]. This paper proposes a strategy for producing suggestions that exploits prior knowledge of preference distributions and can adapt relative to users' reactions to the displayed examples. We evaluate the approach with simulations using data acquired by previous interactions with real users. In two different settings, we measured the effects of prior knowledge and adaptation strategies with satisfactory results.