Expert-Guided Diffusion Planner for Auto-Bidding

TL;DR

This paper introduces an expert-guided diffusion planning method for auto-bidding that enhances decision sequence optimality and efficiency, leading to significant improvements in conversions and revenue in advertising systems.

Contribution

It proposes a novel conditional diffusion model with expert guidance and skip-step sampling, addressing timeliness and personalization issues in auto-bidding.

Findings

11.29% increase in conversions

12.36% growth in revenue

Effective in offline and online experiments

Abstract

Auto-bidding is widely used in advertising systems, serving a diverse range of advertisers. Generative bidding is increasingly gaining traction due to its strong planning capabilities and generalizability. Unlike traditional reinforcement learning-based bidding, generative bidding does not depend on the Markov Decision Process (MDP), thereby exhibiting superior planning performance in long-horizon scenarios. Conditional diffusion modeling approaches have shown significant promise in the field of auto-bidding. However, relying solely on return as the optimality criterion is insufficient to guarantee the generation of truly optimal decision sequences, as it lacks personalized structural information. Moreover, the auto-regressive generation mechanism of diffusion models inherently introduces timeliness risks. To address these challenges, we introduce a novel conditional diffusion modeling approach that integrates expert trajectory guidance with a skip-step sampling strategy to improve generation efficiency. The efficacy of this method has been demonstrated through comprehensive offline experiments and further substantiated by statistically significant outcomes in online A/B testing, yielding an 11.29% increase in conversions and a 12.36% growth in revenue relative to the baseline.