English(EN) Nonparametric Contextual Pricing and Inventory Learning under Censored Demand

新算法解决在线零售定价和库存中的受限需求问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种名为均值校准核UCB（MCK-UCB）的新算法，以应对在线零售定价和库存管理中的挑战。该算法即使在需求数据因缺货而不完整的情况下也能学习到最优策略，这是电子商务中常见的问题。MCK-UCB利用过去相似市场条件下的销售数据来制定明智的定价和库存决策，无需单独的探索阶段即可实现持续学习。该算法已被证明具有 minimax 最优性，并在数值实验中表现出强大的性能。 AI

影响这项研究可能带来更高效的电子商务库存管理和定价策略，从而优化利润和客户满意度。

排序理由该集群包含一篇详细介绍针对特定问题域的新算法的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Zean Han, Jing Liang, Ruihan Lin, Zezhen Ding, Jiheng Zhang · 2026-09-01 04:00

非参数上下文定价与库存学习在审查需求下

arXiv:2608.30944v1 Announce Type: new Abstract: In online retailing, when a product sells out, a retailer often sees only the units sold, not how many customers would have bought it had inventory been available. However, the inventory level determines how much demand is revealed,…

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非参数上下文定价与库存学习在审查需求下

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