Decision-Focused Learning for Bidding Optimization in Online Advertising

Abstract

In large-scale networked systems such as online marketplaces and advertising exchanges, real-time decision-making under dynamic environments is a fundamental challenge. A widely adopted paradigm is the predict-then-optimize workflow, where the prediction stage estimates key factors for decisions, and the optimization stage optimizes objectives based on these factors. However, existing prediction models are typically trained only for prediction accuracy, without awareness of the downstream optimization objective, leading to suboptimal results. We investigate this problem in the context of first-price auction bidding, a canonical decision problem in networked platforms. Specifically, we first propose the Decision-Focused Bidding (DFB) approach, which retains the predict-then-optimize workflow, but integrates the downstream objective into the training of the prediction model. To overcome the zero-gradient issue in back-propagation, we develop a smoothed decision mapping that provides usable gradients while preserving clear interpretability in the bidding context. We further show that DFB's decision-learning objective and the classical objective of learning winning probabilities are complementary under non-i.i.d. environments, and accordingly propose Enhanced DFB (EDFB), which jointly optimizes both objectives to achieve robust generalization in real-world bidding environments. Finally, our EDFB approach has been deployed in a production auto-bidding system, where it achieves a 2.3% GMV lift in a 3-day online A/B test over about 100 billion requests.