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New framework tackles dynamic pricing with strategic buyers and unreliable data

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了一个新颖的上下文动态定价框架，该框架考虑了具有战略性的买家以及不同特征来源的可靠性。源感知局部探索与校准利用（SALE）策略使用经过验证的特征来指导价格探索，并结合了经过验证和报告的特征来减轻战略操纵。这种方法允许从购买反馈中无偏地学习条件平均估值，即使在买家到达率不相同的情况下也能实现一致的估计。该框架在理论上保证了SALE的自恢复特性，并实现了次线性预期遗憾界限，其性能已通过模拟和真实世界数据得到验证。 AI

影响 该框架通过更好地处理用户提供的数据，可以提高电子商务中定价模型的准确性和公平性。

排序理由 学术论文，详细介绍了动态定价的新框架。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

New framework tackles dynamic pricing with strategic buyers and unreliable data

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学术论文，详细介绍了动态定价的新框架。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jinjiang Zou, Yang Li, Zemin Zheng ·

    具有战略性买家的源感知动态定价

    arXiv:2610.22516v1 Announce Type: cross Abstract: In modern commerce, firms and platforms typically use customer features from different sources to personalize prices: some features are verified using seller records or third-party data, while others are self-reported and may be s…

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