Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2004 · 167 citations · 23 references
Open access
BusinessDecision SciencesInformation SystemsDecision MakingDecision Support SystemsMulti-criteria Decision MakingCollaborative FilteringPreference ElicitationPreference ModelingUtility Elicitation MethodsAnalytic Hierarchy ProcessElicitation Of Utility FunctionsIncomplete Preference InformationInteractive Decision SupportInteractive Decision Support SystemsPreference ComparisonUser Preference ModelingUtility Elicitation
As people increasingly rely on interactive decision support systems to choose products and make decisions, building effective interfaces for these systems becomes more and more challenging due to the explosion of on-line information, the initial incomplete user preference and user’s cognitive and emotional limitations of information processing. How to accurately elicit user’s preference thereby becomes the main concern of current decision support systems. This paper is a survey of the typical preference elicitation methods proposed by related research works, starting from the traditional utility function elicitation and analytic hierarchy process methods, to computer aided elicitation approaches which include example critiquing, needs-oriented interaction, comparison matrix, CP-network, preferences clustering & matching and collaborative filtering.
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Paul Resnick, Neophytos Iacovou, Mitesh Suchak et al. · 1994 · 5K citations · Full text
Data mining and knowledge discovery: making sense out of data
U.M. Feyyad · IEEE Expert · 1996 · 4.7K citations
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