Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding
Abstract
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- August 2022
- DOI:
- 10.48550/arXiv.2208.01951
- arXiv:
- arXiv:2208.01951
- Bibcode:
- 2022arXiv220801951H
- Keywords:
-
- Computer Science - Machine Learning;
- Computer Science - Information Retrieval
- E-Print:
- doi:10.1145/3523227.3547383