BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests

2026-09-08

AI Summary

该研究提出Bid-Aware Filter Family (BAFF)来解决在线A/B测试中因共享日志导致的训练数据干扰问题，通过(k,l)-参数化过滤器控制对不同干扰渠道的容忍度。

Abstract

In online A/B tests for real-time bidding (RTB), control and treatment models are typically trained on a shared serving log that includes data generated by the counterpart model. This shared-log training biases each model's training data through two channels: the counterpart model may have selected a different ad from the ad-candidate pool (ad-ranking disagreement) and may have bid a different price (bid-pricing disagreement), potentially distorting the A/B test outcome. Log-splitting eliminates the bias but sacrifices training data; log-sharing retains all data but leaves the bias unaddressed. We formalize the Bid-Aware Filter Family (BAFF), a class of (k,l)-parameterized hard filters that controls tolerance to each channel independently, providing a structured search space between these two extremes. We further propose a three-stage online measurement protocol that enables evaluating data-sharing strategies by their deviation from an interference-free reference model in production. In offline simulation, a (k,l) sweep surfaces operating points with smaller deviation from the interference-free reference model than both log-sharing and log-splitting. In a live RTB deployment on a demand-side platform (DSP), filter-based variants preserve the reference model's business metrics (e.g., CPC, CTR) more closely than both baselines. The best operating point is setting-dependent, underscoring the practical value of the search space itself.

Problem

• Real-time Bidding
• A/B Tests
• Training Data Interference
• Shared Log Training
• Bias

Innovation

• Bid-Aware Filter Family
• RTB A/B Tests
• (k,l)-parameterized hard filters
• data interference mitigation
• online measurement protocol