Enriching buyers' experiences: the SmartClient approach
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Preference-based search and multi-criteria optimization
Eighteenth national conference on Artificial intelligence
Optimal recommendation sets: covering uncertainty over user preferences
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Problem-focused incremental elicitation of multi-attribute tility models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Experiments on the preference-based organization interface in recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
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Preference-based search (PBS) is a popular approach for helping consumers find their desired items from online catalogs. Currently most PBS tools generate search results by a certain set of criteria based on preferences elicited from the current user during the interaction session. Due to the incompleteness and uncertainty of the user's preferences, the search results are often inaccurate and may contain items that the user has no desire to select. In this paper we develop an efficient Bayesian filter based on a group of users' past choice behavior and use it to refine the search results by filtering out items which are unlikely to be selected by the user. Our preliminary experiment shows that our approach is highly promising in generating more accurate search results and saving user's interaction effort.