标题： 基于广义印度自助餐厅过程模型的在线活动预测 显示英文标题

标题： Online activity prediction via generalized Indian buffet process models

摘要： 在线A/B实验每天会生成数百万的用户活动记录，但实验者需要及时的预测来指导发布并保护用户体验。 受亚马逊A/B测试活动预测问题的启发，我们引入了一个贝叶斯非参数模型，用于预测网络实验中的首次触发和重复触发。 该模型基于稳定的beta缩放过程先验，能够在没有严格的参数假设的情况下捕获重尾行为。 所有后验和预测量都可以以封闭形式获得，即使在大规模数据集上也能实现快速推断。 模拟研究和对1,774个生产实验的回顾性分析表明，与最先进的竞争对手相比，在预测新用户和总触发方面具有更高的准确性，特别是在只观察到少数试点日时。 该框架可以在保留校准不确定性估计的同时缩短测试时间。 尽管此方法受亚马逊实验平台的启发，但它可以扩展到其他需要快速、无分布预测稀疏计数过程的应用场景。 显示英文摘要

摘要： Online A/B experiments generate millions of user-activity records each day, yet experimenters need timely forecasts to guide roll-outs and safeguard user experience. Motivated by the problem of activity prediction for A/B tests at Amazon, we introduce a Bayesian nonparametric model for predicting both first-time and repeat triggers in web experiments. The model is based on the stable beta-scaled process prior, which allows for capturing heavy-tailed behaviour without strict parametric assumptions. All posterior and predictive quantities are available in closed form, allowing for fast inference even on large-scale datasets. Simulation studies and a retrospective analysis of 1,774 production experiments show improved accuracy in forecasting new users and total triggers compared with state-of-the-art competitors, especially when only a few pilot days are observed. The framework enables shorter tests while preserving calibrated uncertainty estimates. Although motivated by Amazon's experimentation platform, the method extends to other applications that require rapid, distribution-free prediction of sparse count processes.