Assortment Optimization with a-Similar Substitutes: Insights from Customer Browsing Patterns

• 成果类型: Article
• 署名作者: Chen, Renjie; Jiang, Bo; Ryan, Christopher Thomas; Zhang, Nanxi
• 署名单位: Chinese University of Hong Kong; Shanghai University of Finance & Economics; Shanghai University of Finance & Economics; Shanghai University of Finance & Economics; University of British Columbia; Western University (University of Western Ontario)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.00786
• 发表日期: 2026
• 关键词: assortment optimization; fixed-parameter tractable algorithm; customer choice models

摘要

We propose an approach to model assortment optimization problems based on two observations we made from customer browsing history on Taobao. First, most customers consider very few items (no more than five) before purchasing. Second, there exists a sorting of items so that most customer consideration sets are contained in small intervals in this sorting. This sorting can be discovered by the Cuthill-McKee algorithm, which is designed to work with sparse matrices. We encode these two observations into the alpha-similar substitutes property, which requires that all customers have consideration sets that lie in intervals (in the sorting) of length at most alpha, where alpha is a parameter we select and is fitted from data. The assortment optimization and pricing problems associated with this property are fixed-parameter tractable for a fixed alpha. Moreover, we show that the assortment optimization for some specific choice model with alpha-similar substitutes property is polynomial-time solvable. We demonstrate our approach-going from data to modeling (i.e., selecting an appropriate alpha) and finally to optimization-on another data set of customer click history on JD.com. Lastly, we conduct sensitivity tests on choice models that satisfy the alpha-similar substitutes property in the presence of customers with large consideration sets. We provide an approximation guarantee in terms of revenue when asserting the alpha-similar substitutes property. Both theoretical and numerical results show that the optimal assortment of our estimated model captures most of the revenue even when there are customers with large consideration sets.