Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding
This work addresses the challenge of efficient exploration in high-throughput, low-latency real-time bidding systems, but it appears incremental as it applies an existing uncertainty-based method to a specific domain.
The paper tackled the problem of exploring the supply landscape in real-time bidding by using model uncertainty to direct exploration, resulting in improved model performance and business KPIs as demonstrated through online A/B testing.
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.