English(EN) PPDL: A Real-world Industrial User Retention Ratio Forecasting Framework Integrating Physical Priors with Deep Learning

新的PPDL框架预测短视频平台的用户留存率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了PPDL，一个旨在预测大规模短视频平台用户留存率的新框架。该框架解决了通道异质性、全局衰减和饱和趋势以及有限的回溯窗口等挑战。PPDL通过使用多层感知器建模的威布尔分布分解趋势，并使用辅助嵌入模块处理残差分量以保持通道身份，将物理先验与深度学习相结合。多尺度趋势惩罚损失函数进一步增强了趋势敏感性。在工业规模数据集上的实验表明，PPDL在各种应用中优于现有的在线解决方案。 AI

影响通过提供更准确的留存率预测，该框架可以改善短视频平台的获客预算分配。

排序理由该项目是一篇学术论文，详细介绍了一个预测用户留存率的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zibo Zhao, Zhengxiong Guan, Chaoli Zhang, Linyuan Geng, Xuanbing Zhu, Zhonglong Zheng, Fan Wu · 2026-09-15 04:00

PPDL：一个整合物理先验知识与深度学习的真实工业用户留存率预测框架

arXiv:2609.13789v1 Announce Type: new Abstract: In multi-channel paid user acquisition, early and accurate prediction of user retention at the channel level is crucial for optimizing budget allocation. User retention curves display a pronounced temporal pattern: an initial period…

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报道来源 [1]

PPDL：一个整合物理先验知识与深度学习的真实工业用户留存率预测框架

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