||Interaction Models for Preference Elicitation
||Pratyush Kumar and Pearl Pu
||focus + context, information visualization
||Human centered decision making principles have been the center of massive research activities in the recent past. While on one hand traditional decision theory claims to predict exact solution for a decision problem, provided a value function is defined. A value function is one which can predict accurately about decision values given precise value of the attributes. Researchers have long found the problem of defining a precise value function tough in real life scenarios. The problem becomes even more complicated when the decision task is a multi attribute function. A simple example of such a task is the selection of a multi attribute product. Our work centers around selection of a multi attribute product on an e-store. Many tools are currently available which help users in making the crucial decision. The major impediment with such tools is the fact that under uncertainty in preferences, they are only as good in helping the users in their decision task, as users themselves are in their preferences. Experiments done with users have suggested the fact that often preferences with users are fluid and uncertain, hence a proper method has to be designed to elicit those unformed and often unspecified preferences. It is these preferences that lead to a definite decision goal. Under the scenario of uncertain and unspecified preferences, the task of defining a precise value function also becomes highly complex and difficult. We believe that such hidden and unstated preferences can be elicited by providing users with example. This process is called example critiquing. The users are shown examples of the products they are looking for, and then they discover their preferences when attributes of the example violated any of their hidden preferences. The idea is that users do not know their preferences till they see them being violated. Thus, we believe that such example critiquing can be crucial in eliciting preferences of users and help them in better decision making.
||Swiss National Science Foundation
||Main page; Demo 1: RankedListUI; Demo 2: TweakingListUI