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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Interactive Critiquing forCatalog Navigation in E-Commerce
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The FindMe Approach to Assisted Browsing
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ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Making Rational Decisions Using Adaptive Utility Elicitation
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A POMDP formulation of preference elicitation problems
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Evaluating example-based search tools
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WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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Proceedings of the 6th ACM conference on Electronic commerce
Increasing user decision accuracy using suggestions
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Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System
IEEE Intelligent Systems
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Evaluating critiquing-based recommender agents
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On the evaluation of dynamic critiquing: a large-scale user study
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing
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A live-user evaluation of incremental dynamic critiquing
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Improving Decision Quality Through Preference Relaxation
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Preferences in AI: An overview
Artificial Intelligence
Intelligent product search with soft-boundary preference relaxation
Expert Systems with Applications: An International Journal
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
On-line dynamic adaptation of fuzzy preferences
Information Sciences: an International Journal
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Conversational recommenders can help users find their most preferred item among a large range of options, a task that we call preference-based search. Motivated by studies in the field of behavioral decision theory, we take a user centric design perspective, focusing on the trade-off between decision accuracy and user effort. We consider example-critiquing, a methodology based on showing examples to the user and acquiring preferences in the form of critiques. In our approach critiques are volunteered in a mixed-initiative interaction. Some recommendations are suggestions specifically aimed at stimulating preference expression to acquire an accurate preference model. We propose a method to adapt the suggestions according to observations of the user's behavior. We evaluate the decision accuracy of our approach with both simulations exploiting logs of previous users of the system (in order to see how adaptive suggestions improve the process of preference elicitation) and surveys with real users where we compare our approach of example critiquing with an interface based on question-answering.