# Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising

arXiv: https://arxiv.org/abs/2605.21556 · HTML: https://mlsanity.com/paper/2605.21556/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 2 of 2)

- [Percentile Risk-Constrained Budget Pacing for Guaranteed Display Advertising in Online Optimization](https://mlsanity.com/paper/2312.06174/) (2023)
- [Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization](https://mlsanity.com/paper/2508.02002/) (2025)

## Built on by (most cited first, 1 of 1)

- [HMAF: A Hierarchical Multi-Slot GD-RTB Allocation Framework](https://mlsanity.com/paper/2606.09896/) (2026)

## Abstract (arXiv)

Guaranteed display advertising is crucial for platform monetization, yet existing methods often operate under a single-slot assumption, limiting their ability to optimize allocation across multi-slot page views. In this paper, we propose a novel joint optimization framework for multi-slot GD allocation, addressing key challenges such as slot-level redundancy, contract imbalance, and exposure concentration. Our approach formulates the allocation as an offline bipartite matching problem with a contract roulette mechanism for slot exclusivity and Page View constraints for impression control, and incorporates a scalable allocation optimization algorithm for efficient large-scale deployment. Extensive online tests on the Meituan advertising platform demonstrate that our method significantly improves merchant ROI, platform revenue efficiency, and contract fulfillment robustness. Specifically, online A/B tests show a 28.99% increase in Average Revenue Per User under 70% traffic, and DID analysis further indicates improved contract stability, demonstrating the strong applicability and effectiveness of our framework in real-world advertising deployments.