Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization

TL;DR

This paper introduces GRAD, a scalable generative model for automated ad bidding that improves revenue and ROI by addressing challenges in online advertising environments.

Contribution

The paper presents GRAD, a novel foundation model combining a mixture-of-experts and causal transformer for constraint-aware, diverse, and scalable ad bidding optimization.

Findings

GRAD increases platform revenue significantly.

Implementation in Meituan led to 2.18% GMV growth.

ROI improved by 10.68% with GRAD.

Abstract

Modern auto-bidding systems are required to balance overall performance with diverse advertiser goals and real-world constraints, reflecting the dynamic and evolving needs of the industry. Recent advances in conditional generative models, such as transformers and diffusers, have enabled direct trajectory generation tailored to advertiser preferences, offering a promising alternative to traditional Markov Decision Process-based methods. However, these generative methods face significant challenges, such as the distribution shift between offline and online environments, limited exploration of the action space, and the necessity to meet constraints like marginal Cost-per-Mille (CPM) and Return on Investment (ROI). To tackle these challenges, we propose GRAD (Generative Reward-driven Ad-bidding with Mixture-of-Experts), a scalable foundation model for auto-bidding that combines an Action-Mixture-of-Experts module for diverse bidding action exploration with the Value Estimator of Causal Transformer for constraint-aware optimization. Extensive offline and online experiments demonstrate that GRAD significantly enhances platform revenue, highlighting its effectiveness in addressing the evolving and diverse requirements of modern advertisers. Furthermore, GRAD has been implemented in multiple marketing scenarios at Meituan, one of the world's largest online food delivery platforms, leading to a 2.18% increase in Gross Merchandise Value (GMV) and 10.68% increase in ROI.