2024年度数字运营管理微论坛

Seminar format:

Tencent Meeting

Speakers:

Leading scholars on digital operation management

Audience:

Open to public

Language:

Chinese (mostly) or English up to speakers

Sponsors:

Southeast University School of Economics and Management

Co-Chairs:

Sijie Li (Southeast University)

Jing Chen (Southeast University)

Time:

2024/05/05（Sun.） 9:00    (上午AM)

Meeting No.:

909-178-097 (Tencent)

报告一：

Assortment Optimization for the Multinomial Logit Model with Repeated Customer Interactions

讲座人：

Prof. Ningyuan Chen

Rotman School of Management

University of Toronto

摘要：

This paper presents the multinomial logit model with repeated customer interactions. In each period, the same customer selects a product from the assortment recommended in that period or opts out. It captures the essence of an increasingly popular business model called the subscription box, exemplified by Stitch Fix and Wantable. From the seller's perspective, the choice probability is updated based on the purchase history. We study the adaptive assortment recommendation strategy for all the periods. Although the problem is generally NP-hard as we show, when the customer interacts with the seller for two periods, we discover the structures of the optimal assortment when the available products in the two periods are identical and develop approximation algorithms in other cases. For more than two periods, although the optimal assortments are intractable, we find that the optimal fixed assortments that are not adapted to the purchase history can achieve 68.47% or 50% of the optimal expected revenue, respectively, when the available products across periods are disjoint or not. Using two public datasets, we demonstrate that the model with repeated customer interactions can better predict the purchase behavior and generates higher revenues.

嘉宾简介:

Dr. Ningyuan Chen is currently an assistant professor at the Department of Management at the University of Toronto, Mississauga and cross appointed at the Rotman School of Management, University of Toronto. Before joining the University of Toronto, he was an assistant professor at the Hong Kong University of Science and Technology. Prior to that, he was a postdoctoral fellow at the Yale School of Management. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. He is interested in various approaches to making data-driven decisions in business applications such as revenue management. His studies have been published in Management Science, Operations Research, Annals of Statistics, and other journals. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada. He is the recipient of the Roger Martin Award for Excellence in Research and the IMI Research Award.

报告二：

Modeling and Optimization on Sequential Choice and Consumer Rationality

讲座人：

Prof. Ruxian Wang     

Johns Hopkins University

摘要：

Under classic discrete choice models, consumers observe all available products simultaneously and select the one with the highest utility (e.g., the widely used MNL and NL model). Despite the tremendous importance of one-stage simultaneous choice models in theory and practice, they fail to capture the ubiquitous sequential choice behaviors, which have been often observed in customers' purchase behaviors on online platforms (e.g., Amazon, Booking.com). In this paper, we propose a novel sequential choice model with representative products to describe customers' purchase behaviors, where the product set includes several nests and each nest has a representative product. Customers make their purchase decision sequentially following the product information disclosure. Under this sequential choice model, we investigate various associated operations management problems.

嘉宾简介:     

Dr. Ruxian Wang is a Professor at Johns Hopkins University, Carey Business School. Before returning to academia, he worked in Hewlett-Packard Company for several years as a research scientist. He received Ph.D. from Columbia University. His research and teaching interests include operations management, revenue management, pricing, discrete choice models, data-driven decision making. His articles appeared in several flagship journals in his field, such as Management Science, Manufacturing & Service Operations Management, Operations Research, Production and Operations Management.

报告三：

Prophet Inequalities for Product Recommendation under the Bounded Last-Choice Model

讲座人：

Prof. Pin Gao     

School of Data Science

Chinese University of Hong Kong, Shenzhen

摘要：

This paper introduces a novel regular choice model that merely imposes an upper bound on each product's last-choice probability (i.e., the purchase probability of being exclusively recommended). Notable instances of this model include the Multinomial Logit (MNL), click-based MNL, sequential click-based MNL, and the general attraction model. For the static cardinality-constrained revenue-maximizing recommendation problem, despite its NP-hardness even under specific model instances, we propose a polynomial-time solvable constant-factor approximation heuristic for the general case, leveraging solely the last-choice probabilities. Additionally, we investigate an online recommendation scenario inspired by the burgeoning practice of live-streaming e-commerce. In this context, product-specific parameters are sequentially revealed as random and independent variables, compelling decision-makers to make immediate and irreversible decisions regarding whether to recommend a product upon receiving its information. We offer constant-factor approximations for cases where the information arrival order is adversarial or adheres to specific distributional patterns. Our comparative performance analysis underscores the importance of information in the online setting and establishes a novel connection between the online recommendation problem and the classical multiple-unit prophet inequality.

嘉宾简介:     

Dr. Pin Gao is an assistant Professor at the School of Data Science, the Chinese University of Hong Kong, Shenzhen. He received his B.S. in Physics from Wuhan University and his MPhil in Physics and Ph.D in Operations Research from The Hong Kong University of Science and Technology. Dr. Gao's research interests include revenue management and operations management in emerging business models. His papers have been published in Management Science, Operations Research, Manufacturing & Service Operations Management and Wine. He has received multiple research awards such as the ISCOM best paper (first place) and the POMS-China best paper (second place).