【Topic】Two New Prediction-driven Approaches to Discrete Choice Prediction

【Speaker】Huimin Zhao, Associate Professor of information technology management in the Sheldon B. Lubar School of Business at the University of Wisconsin-Milwaukee

【Time】June 5th, Wednesday, 15:30-17:00

【Venue】Room 305, Weilun Building, Tsinghua SEM 

【Language 】English

【Organizer】Department of Management Science and Engineering

【Abstract】

The ability to predict consumer choices is essential in understanding the demand structure of products and services. Typical discrete choice models that are targeted at providing an understanding of the behavioral process leading to choice outcomes are developed around two main assumptions: the existence of a utility function that represents the preferences over a choice set and the relatively simple and interpretable functional form for the utility function with respect to attributes of alternatives and decision makers. These assumptions lead to models that can be easily interpreted to provide insights into the effects of individual variables, such as price and promotion, on consumer choices. However, these restrictive assumptions might impede the ability of such theory-driven models to deliver accurate predictions and forecasts. In this work, we develop novel approaches targeted at providing more accurate choice predictions. Specifically, we propose two prediction-driven approaches: pairwise preference learning using classification techniques and ranking function learning using evolutionary computation. We compare our proposed approaches with a multi-class classification approach, as well as a standard discrete choice model. Our empirical results show that the proposed approaches achieved significantly higher choice prediction accuracy.

【Bio】

Huimin Zhao received the B.E. and M.E. degrees in automation from Tsinghua University in 1990 and 1993, respectively, and the Ph.D. degree in management information systems from the University of Arizona, USA in 2002. He is an Associate Professor of information technology management in the Sheldon B. Lubar School of Business at the University of Wisconsin-Milwaukee, USA. His current research interests include data mining and recommendation systems. He has published in such journals as Communications of the ACM, ACM Transactions on Management Information Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, Information Systems, and Journal of Management Information Systems. He serves as an associate editor on the editorial board of MIS Quarterly.