论文标题
实时投标的模型不确定性探索模型不确定性
Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding
论文作者
论文摘要
在这项工作中，我们提出了一个可扩展有效的系统，用于探索实时招标的供应格局。该系统根据用于点击率预测的模型的预测不确定性来指导探索，并在高通量，低延迟环境中起作用。通过在线A/B测试，我们证明了使用模型不确定性的探索对模型性能和业务KPI有积极影响。
In this work, we present a scalable and efficient system for exploring the supply landscape in real-time bidding. The system directs exploration based on the predictive uncertainty of models used for click-through rate prediction and works in a high-throughput, low-latency environment. Through online A/B testing, we demonstrate that exploration with model uncertainty has a positive impact on model performance and business KPIs.
