Distillation-Accelerated Uncertainty Modeling for Multi-Objective RTA Interception
This work addresses efficiency bottlenecks in uncertainty modeling for real-time traffic filtering in advertisement systems, representing an incremental improvement.
The paper tackled the problem of accurately estimating traffic quality with reliable confidence in Real-Time Auction Interception, proposing DAUM, a framework that integrates multi-objective learning with uncertainty modeling, and achieved a tenfold increase in inference speed through knowledge distillation while maintaining predictive performance.
Real-Time Auction (RTA) Interception aims to filter out invalid or irrelevant traffic to enhance the integrity and reliability of downstream data. However, two key challenges remain: (i) the need for accurate estimation of traffic quality together with sufficiently high confidence in the model's predictions, typically addressed through uncertainty modeling, and (ii) the efficiency bottlenecks that such uncertainty modeling introduces in real-time applications due to repeated inference. To address these challenges, we propose DAUM, a joint modeling framework that integrates multi-objective learning with uncertainty modeling, yielding both traffic quality predictions and reliable confidence estimates. Building on DAUM, we further apply knowledge distillation to reduce the computational overhead of uncertainty modeling, while largely preserving predictive accuracy and retaining the benefits of uncertainty estimation. Experiments on the JD advertisement dataset demonstrate that DAUM consistently improves predictive performance, with the distilled model delivering a tenfold increase in inference speed.