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.
翻译:在这项工作中,我们提出了一个在实时招标中探索供应格局的可扩展和高效的系统,该系统根据用于点击通速率预测的模型的预测不确定性和在高通量、低纬度环境中的工程进行勘探,通过在线A/B测试,我们证明,具有模型不确定性的勘探对模型性能和商业《国际投资准则》产生了积极影响。