Assortment Planning for Recommendations at Checkout Under Inventory Constraints

• 成果类型: Article
• 署名作者: Chen, Xi; Ma, Will; Simchi-Levi, David; Xin, Linwei
• 署名单位: New York University; Columbia University; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Chicago
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.2023.1357
• 发表日期: 2024
• 关键词: revenue management; stochastic knapsack; choice model; optimization; approximation; algorithms; auctions; demand

摘要

In this paper, we consider a personalized assortment planning problem under inventory constraints, where each arriving customer type is defined by a primary item of interest. As long as that item is in stock, the customer adds it to the shopping cart, at which point the retailer can recommend to the customer an assortment of add-ons to go along with the primary item. This problem is motivated by the new recommendation at checkout systems that have been deployed at many online retailers, and it also serves as a framework that unifies many existing problems in online algorithms (e.g., personalized assortment planning, single-leg booking, and online matching with stochastic rewards). In our problem, add-on recommendation opportunities are eluded when primary items go out of stock, which poses additional challenges for the development of an online policy. We overcome these challenges by introducing the notion of an inventory protection level in expectation and derive an algorithm with a 1/4-competitive ratio guarantee under adversarial arrivals.