arXiv cs.AI· Xin Heng·· 12 小时前AI 评分18
StoBasket：面向贡献毛利的追加销售与交叉销售联合优化
StoBasket: Joint Upsell and Cross-Sell Optimization for Contribution Margin
AI 导读
StoBasket 提出联合随机购物篮编辑框架，用一个策略同时为现有购物篮选择替换与加购报价，按预期增量贡献毛利估值，并首次将追加销售与交叉销售响应耦合建模。在五个公开购物篮数据集的真实数据校准模拟中，StoBasket 在每种默认价格设置下都捕获了可达报价毛利的 0.5% 以内，真实响应落在模型族外时也在 0.7% 以内。
正文 · 原文
Abstract:Retail recommenders rank relevance, but retailers earn contribution margin: an upsell earns the margin difference over the item it replaces, whereas a cross-sell earns its full margin. We introduce joint stochastic basket editing, a formulation in which one policy selects replacement and addition offers for an existing basket, values them by expected incremental contribution margin, and couples them through displayed-offer, accepted-offer, and joint-acceptance response channels. To our knowledge, it is the first formulation in which upsell and cross-sell responses are coupled; prior separable treatments are its zero-coupling special case. In real-data-calibrated simulations on five public basket datasets, StoBasket captures essentially all attainable offer margin: within 0.5% in every default-price setting and within 0.7% when the true response lies outside its model family. Correct economic accounting is the broadest gain: margin-weighted popularity leaves up to 11% of attainable margin unrealized, and a published next-basket recommender leaves 11-52%. Joint selection adds 10.2% of attainable margin on Instacart, where premium tiers and add-ons are strongly linked. When offers pay only together, the best heuristics lose 64% while exact search stays within 0.5%. For a structured subclass, we characterize when staged policies fail and give an exact optimizer that solves every tested real basket in 0.13 ms median and at most 111 ms. Profit saturates long before prediction accuracy: a correctly structured predictor trained on 5,000 randomized sessions comes within 0.06% of perfect prediction, while a signal-free one loses 38%. The same estimates yield a decision-aware market-basket analysis whose cost does not grow with association order.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.22768 [cs.AI] |
| (or arXiv:2610.22768v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.22768 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xin Heng [view email]
[v1]
Fri, 16 Oct 2026 00:19:43 UTC (112 KB)
来源：arXiv cs.AI · arxiv.org