English(EN) Self-Improving Neural Pruning: A Graph Neural Network Framework for Scalable Mixed Bundle Pricing

图神经网络增强混合捆绑定价策略

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-02 04:00

研究人员开发了一个新颖的图神经网络（GNN）框架，旨在解决计算上具有挑战性的混合捆绑定价问题。该方法将客户-产品关系编码为图，并使用GNN预测产品分配概率，然后用于剪枝候选捆绑包。该框架包括一个GNN引导的局部搜索和一个迭代的自改进过程，以优化更大实例的解决方案。实验表明，该方法在较小的数据集上可以恢复超过98%的最优利润，并在较大实例上优于现有的捆绑包大小定价策略，同时显著减少了运行时间。 AI

影响该GNN框架为混合捆绑定价提供了一种更有效的方法，可能影响各行业的收入管理。

排序理由关于针对特定优化问题的新GNN框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Liangyu Ding, Guokai Li, Zizhuo Wang, Chenghan Wu · 2026-07-02 04:00

自适应改进的神经剪枝：用于可扩展混合捆绑定价的图神经网络框架

arXiv:2509.22557v3 Announce Type: replace Abstract: Mixed bundle pricing is a classic revenue management problem arising in industries such as e-commerce, tourism, and video games. It refers to designing product combinations (i.e., bundles) and determining their prices to maximiz…

本文如何被排名

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自适应改进的神经剪枝：用于可扩展混合捆绑定价的图神经网络框架

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