A0950
Title: Doubly high-dimensional contextual bandits: An interpretable model for joint assortment and pricing
Authors: Ran Chen - Washington University in St. Louis (United States) [presenting]
Junhui Cai - University of Notre Dame (United States)
Martin Wainwright - Massachusetts Institute of Technology (United States)
Linda Zhao - University of Pennsylvania (United States)
Abstract: The rapid growth in data availability, the vast need for decision-making, and advancements in machine learning and statistics have made data-driven decision-making possible and unprecedentedly important. In high-stake fields, such as business and healthcare, decision-makers face more challenges: managing high dimensionality of data, balancing interpretability with performance, ensuring computational efficiency and statistical accuracy, and adhering to domain-specific principles. These multifaceted challenges call for innovative approaches in modeling, methodology, and theory. The focus is on doubly high-dimensional contextual bandits. The motivation is a real-world challenge: The joint assortment and pricing problem faced by an industry-leading instant noodles company, where decisions need to be made about product offerings and their pricing simultaneously. To address this problem, a novel model of doubly high-dimensional contextual bandits is proposed to capture this sequential decision-making problem. An efficient algorithm is proposed for this interpretable yet flexible model. Their power is showcased through theoretical guarantees, case studies, and simulation studies. Further discussions on personalized reinforcement learning may be presented.
