A Lightweight MPC Bidding Framework for Brand Auction Ads

2026-03-08

AI Summary

This work addresses the lack of efficient bidding algorithms in brand advertising auctions that can fully exploit the stability of user engagement and the rapid feedback inherent in such campaigns. We propose a lightweight model predictive control (MPC) framework that, for the first time, integrates online isotonic regression with MPC to construct monotonic bid–spend and bid–conversion relationships in real time from streaming data. This approach enables accurate, low-overhead real-time bidding without relying on complex models. Extensive simulations demonstrate that our method significantly outperforms existing baselines, achieving superior performance in both cost control and spending efficiency. Moreover, the framework exhibits high scalability and strong practical viability for real-world deployment.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Brand advertising plays a critical role in building long-term consumer awareness and loyalty, making it a key objective for advertisers across digital platforms. Although real-time bidding has been extensively studied, there is limited literature on algorithms specifically tailored for brand auction ads that fully leverage their unique characteristics. In this paper, we propose a lightweight Model Predictive Control (MPC) framework designed for brand advertising campaigns, exploiting the inherent attributes of brand ads -- such as stable user engagement patterns and fast feedback loops -- to simplify modeling and improve efficiency. Our approach utilizes online isotonic regression to construct monotonic bid-to-spend and bid-to-conversion models directly from streaming data, eliminating the need for complex machine learning models. The algorithm operates fully online with low computational overhead, making it highly practical for real-world deployment. Simulation results demonstrate that our approach significantly improves spend efficiency and cost control compared to baseline strategies, providing a scalable and easily implementable solution for modern brand advertising platforms.

Problem

• brand advertising
• real-time bidding
• auction ads
• spend efficiency
• cost control

Innovation

• Model Predictive Control
• online isotonic regression
• brand advertising
• real-time bidding
• lightweight framework