论文标题
BBE-LSWCM：长窗口单击模型的自举集合
BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models
论文作者
论文摘要
我们考虑为实时客户事件预测问题（例如QBO）中的实时客户事件预测问题开发ClickStream建模框架的问题。我们开发了低延迟，具有成本效益和健壮的集成体系结构（BBE-LSWCM），该体系结构（BBE-LSWCM）结合了从较长的历史窗口（例如，在过去的几周中）以及最近past的一个简短窗口（例如，在当前会议中，在当前会议中）中的用户活动结合了两个汇总的用户行为数据。与其他基线方法相比，我们证明了提出的方法在两个重要的实时事件预测问题上的出色性能：QBO订阅者的订阅取消和预期的任务检测。最后，我们介绍了QBO中的实时部署的详细信息和在线实验的结果。
We consider the problem of developing a clickstream modeling framework for real-time customer event prediction problems in SaaS products like QBO. We develop a low-latency, cost-effective, and robust ensemble architecture (BBE-LSWCM), which combines both aggregated user behavior data from a longer historical window (e.g., over the last few weeks) as well as user activities over a short window in recent-past (e.g., in the current session). As compared to other baseline approaches, we demonstrate the superior performance of the proposed method for two important real-time event prediction problems: subscription cancellation and intended task detection for QBO subscribers. Finally, we present details of the live deployment and results from online experiments in QBO.
