工商管理学院建院30周年
系列学术报告 第11场
报告题目：Joint Optimization of Pricing and Personalized Recommendations in Online Retailing
报告人：Leeds School of Business, University of Colorado Boulder
报告时间：2024年6月6日（周四）15:30~17:00
报告地点：文管学馆B545
报告摘要：
We study the problem of pricing and personalized recommendations in online retailing. A set of products with a fixed starting inventory is offered to different types of customers. The prices can vary over time but need to be consistent across customer groups at all times. Customers’ purchase decisions are also influenced by personalized product recommendations. We study a setting where the products are assumed to be independent (i.e., no substitution). However, there is a limit on the number of products that can be recommended to each customer at any given time. We formulate the problem as a finite-horizon stochastic dynamic program. Due to the constraint on the number of recommended products for each customer, the problem is not separable by product. We propose a solution strategy based on Lagrangian relaxation. We show that the linear programming formulation of the Lagrangian relaxation admits a compact reformulation. Solving the compact reformulation is much more computationally efficient than alternative methods to solve the Lagrangian dual. We further prove a performance guarantee for a heuristic policy based on the solution of the compact reformulation. The policies and bounds are validated with data from a leading online retailer in China. We demonstrate that the proposed policies can achieve significant revenue improvement (over 7%), compared to a policy reflecting the retailer’s current practice. We also examine the relative value of personalized recommendations and dynamic pricing; dynamic pricing is shown to be highly valuable, while the value of personalized recommendations is relatively smaller yet still practically significant.
报告人简介：
Rui Zhang is an associate professor in the Strategy, Entrepreneurship, and Operations division at Leeds School of Business, University of Colorado Boulder. He is the Faculty Director of the Master’s Program in Business Analytics. Before that, he served as the Director of the Ph.D. Program in Operations. Furthermore, he is serving as Associate Editor for INFORMS Journal on Computing and Networks. His research interests are quantitative methods, especially prescriptive analytics techniques. His work focuses on revenue management problems, last-mile delivery, and influence maximization problems on social networks. His work has been published in Operations Research, Manufacturing & Service Operations Management, INFORMS Journal on Computing, INFORMS Journal on Optimization, Naval Research Logistics, European Journal of Operational Research, and Networks, among others. In addition, he has won several Best Paper awards. A collection of his work was selected as the runner-up for the 2022 INFORMS Computing Society (ICS) Prize.
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