CISE Seminar: Junyu Cao, The University of Texas at Austin

Date: Friday, September 22, 2023
Time: 3:00pm – 4:00pm
Location: 665 Commonwealth Avenue, CDS 1101

Assistant Professor Junyu Cao, The University of Texas at Austin

Junyu Cao
Assistant Professor, Department of Information, Risk, and Operations Management (Decision Science)
The University of Texas at Austin

Collaborative Learning and Decision-Making on Pricing and Recommendation: A Simple Framework for Planning
We formulate a collaborative learning and decision-making problem involving contextual information. In current business practices, pricing and recommendation decisions often are made jointly by multiple teams in sequence. The decision-making processes for different teams can be controlled by either a centralized or decentralized planner. We propose a simple collaboration framework that integrates the learning about decision-making in an unknown environment. The main challenge in a decentralized framework is that the decision-making process in other teams is unknown, but the subsequent decisions are mutually dependent. From practical concern of high exploring cost and implementation complexity, we propose a simple greedy algorithm for the centralized planner and a “greedy” + “weighted sampling” (GWS) algorithm for both the centralized and decentralized planner to balance the learning and earning. We surprisingly show that the exploration-free greedy algorithm can achieve the optimal rate when context diversity holds. The GWS algorithm works effectively for either centralized or decentralized planners under a much weaker condition, which we call context variation. Furthermore, we extend our framework to the multi-product pricing and ranking problem and study the model misspecification issue. We test our algorithm using real data from JD.com, a large e-commerce retailer. Numerical studies validate the superior performance of the two proposed frameworks for different types of planners.

Junyu Cao is an Assistant Professor in the Department of Information, Risk, and Operations Management at McCombs School of Business, The University of Texas at Austin. She received her Ph.D. in the Department of Industrial Engineering and Operations Research (IEOR) from University of California, Berkeley in 2020. Her current research interests include: 1) Data-driven decision making and online learning, with a focus on revenue management and recommendation systems; 2) Stochastic modeling, with applications to the smart-city analytics and operations. It specifically includes the agile cities (e.g. retail on wheels) and the city logistics (e.g., last-mile delivery).

Faculty Host: Jinglong Zhao
Student Host: Ehsan Sabouni