2017/04/01 by Wang Chi Cheung, David Simchi-Levi, David Simchi‐Levi +2 · 1 citation
Business, Management and Accounting · Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Optimization and Search Problems #Supply Chain and Inventory Management #cs.LG
paper · pdf · doi:10.48550/arxiv.1704.00108
16 pages, 2 figures
arxiv created 2017/04/01 · openalex publication_date 2017/04/01 · arxiv updated 2017/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motivated by e-commerce, we study the online assortment optimization problem. The seller offers an assortment, i.e. a subset of products, to each arriving customer, who then purchases one or no product from her offered assortment. A customer's purchase decision is governed by the underlying MultiNomial Logit (MNL) choice model. The seller aims to maximize the total revenue in a finite sales horizon, subject to resource constraints and uncertainty in the MNL choice model. We first propose an efficient online policy which incurs a regret O(T2/3), where T is the number of customers in the sales horizon. Then, we propose a UCB policy that achieves a regret O(T1/2). Both regret bounds are sublinear in the number of assortments.