# Feedback Control of Real-Time Display Advertising

arXiv: https://arxiv.org/abs/1603.01055 · HTML: https://mlsanity.com/paper/1603.01055/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2016.

## Built on by (most cited first, 22 of 22)

- [AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games](https://mlsanity.com/paper/2412.10798/) (2024)
- [Offline Reinforcement Learning for Optimizing Production Bidding Policies](https://mlsanity.com/paper/2310.09426/) (2023)
- [Bidding Agent Design in the LinkedIn Ad Marketplace](https://mlsanity.com/paper/2202.12472/) (2022)
- [We Know What You Want: An Advertising Strategy Recommender System for Online Advertising](https://mlsanity.com/paper/2105.14188/) (2021)
- [An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising](https://mlsanity.com/paper/2502.05187/) (2025)
- [Analysis of Dual-Based PID Controllers through Convolutional Mirror Descent](https://mlsanity.com/paper/2202.06152/) (2022)
- [Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)](https://mlsanity.com/paper/2412.11434/) (2024)
- [A Real-World Implementation of Unbiased Lift-based Bidding System](https://mlsanity.com/paper/2202.13868/) (2022)
- [Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes](https://mlsanity.com/paper/2212.13923/) (2022)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [Bid2X: Revealing Dynamics of Bidding Environment in Online Advertising from A Foundation Model Lens](https://mlsanity.com/paper/2510.23410/) (2025)
- [Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning](https://mlsanity.com/paper/2105.10587/) (2021)
- [Cost-Control in Display Advertising: Theory vs Practice](https://mlsanity.com/paper/2409.03874/) (2024)
- [Feedback Control for Small Budget Pacing](https://mlsanity.com/paper/2509.25429/) (2025)
- [Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies](https://mlsanity.com/paper/2601.02754/) (2026)
- [SEGB: Self-Evolved Generative Bidding with Local Autoregressive Diffusion](https://mlsanity.com/paper/2602.22226/) (2025)
- [Knowledge-informed Bidding with Dual-process Control for Online Advertising](https://mlsanity.com/paper/2603.04920/) (2026)
- [A Lightweight MPC Bidding Framework for Brand Auction Ads](https://mlsanity.com/paper/2603.07721/) (2026)
- [Generative Auto-Bidding with Unified Modeling and Exploration](https://mlsanity.com/paper/2605.19457/) (2026)
- [Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation](https://mlsanity.com/paper/2605.26424/) (2026)
- [Constrained Auto-Bidding via Generative Response Modeling](https://mlsanity.com/paper/2605.27811/) (2026)
- [HMAF: A Hierarchical Multi-Slot GD-RTB Allocation Framework](https://mlsanity.com/paper/2606.09896/) (2026)

## Abstract (arXiv)

Real-Time Bidding (RTB) is revolutionising display advertising by facilitating per-impression auctions to buy ad impressions as they are being generated. Being able to use impression-level data, such as user cookies, encourages user behaviour targeting, and hence has significantly improved the effectiveness of ad campaigns. However, a fundamental drawback of RTB is its instability because the bid decision is made per impression and there are enormous fluctuations in campaigns' key performance indicators (KPIs). As such, advertisers face great difficulty in controlling their campaign performance against the associated costs. In this paper, we propose a feedback control mechanism for RTB which helps advertisers dynamically adjust the bids to effectively control the KPIs, e.g., the auction winning ratio and the effective cost per click. We further formulate an optimisation framework to show that the proposed feedback control mechanism also has the ability of optimising campaign performance. By settling the effective cost per click at an optimal reference value, the number of campaign's ad clicks can be maximised with the budget constraint. Our empirical study based on real-world data verifies the effectiveness and robustness of our RTB control system in various situations. The proposed feedback control mechanism has also been deployed on a commercial RTB platform and the online test has shown its success in generating controllable advertising performance.