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

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.

Accepted as a poster presentation at the OARS Workshop @RecSys 2026. 9 pages, 1 figure, 7 tables

Machine Learning (cs.LG)

arXiv:2609.08725 [cs.LG]

(or arXiv:2609.08725v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.08725 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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