Jiyong Zhang
Pearl Pu
ABSTRACT
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.
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Index Terms
- Refining preference-based search results through Bayesian filtering
Recommendations
Preference-based search with adaptive recommendations
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Motivated by studies in the field of behavioral decision theory, we take a user centric design ...
Preference-based graphic models for collaborative filtering
UAI'03: Proceedings of the Nineteenth conference on Uncertainty in Artificial IntelligenceCollaborative filtering is a very useful general technique for exploiting the preference patterns of a group of users to predict the utility of items to a particular user. Previous research has studied several probabilistic graphic models for ...
User preference through Bayesian categorization for recommendation
PRICAI'06: Proceedings of the 9th Pacific Rim international conference on Artificial intelligenceThe personalized recommendation system is required to save efforts in searching the items in ubiquitous commerce, it is very important for a recommendation system to predict accurately by analyzing user's preferences. A recommendation system utilizes in ...
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