Utility-Based Decision Tree Optimization: A Framework for Adaptive Interviewing

Abstract

An emerging practice in e-commerce systems is to conduct interviews with buyers in order to identify their needs. The goal of such an interview is to determine sets of items that match implicit requirements. Decision trees structure the interview process by defining which question follows a given answer. One problem related to decision trees is that changes in the selling strategy or product mix require complex tree restructuring efforts. In this paper we present a framework that represents the selling strategy as a set of parameters, reflecting the preferences of sellers and buyers. This representation of the strategy can be used to generate optimized decision trees in an iterative process, which exploits information about historical buyer behavior. Furthermore, the framework also supports advanced optimization strategies such as dynamic parameter adaptation and exit risk minimization.