Customizing Question Selection in Conversational Case-Based Reasoning

Conversational case-based reasoning systems use an interactive dialog to retrieve stored cases. Normally the ordering of questions in this dialog is chosen based only on their discriminativeness. However, because the user may not be able to answer all questions, even highly discriminative questions are not guaranteed to provide information. This paper presents a customization method CCBR systems can apply to adjust entropy-based discriminativeness considerations by predictions of user ability to answer questions. The method uses a naive Bayesian classifier to classify users into user groups based on the questions they answer, applies information from group profiles to predict which future questions they are likely to be able to answer, and selects the next questions to ask based on a combination of information gain and response likelihood. The method was evaluated for a mix of simulated user groups, each associated with particular probabilities for answering questions about each case indexing feature, in four sample domains. For simulated users with varying abilities to answer particular questions, results showed improvement in dialog length over a non-customized entropy-based approach in all test domains.