# Optimal Auction Design in the Joint Advertising

arXiv: https://arxiv.org/abs/2507.07418 · HTML: https://mlsanity.com/paper/2507.07418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Optimal Auctions through Deep Learning: Advances in Differentiable Economics](https://mlsanity.com/paper/1706.03459/) (2017)
- [Complexity of Mechanism Design](https://mlsanity.com/paper/1408.1486/) (2014)
- [A Permutation-Equivariant Neural Network Architecture For Auction Design](https://mlsanity.com/paper/2003.01497/) (2020)
- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022)
- [A Scalable Neural Network for DSIC Affine Maximizer Auction Design](https://mlsanity.com/paper/2305.12162/) (2023)
- [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021)
- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [Certifying Strategyproof Auction Networks](https://mlsanity.com/paper/2006.08742/) (2020)
- [GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning](https://mlsanity.com/paper/2406.07428/) (2024)
- [Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising](https://mlsanity.com/paper/2012.02930/) (2020)
- [Selling Joint Ads: A Regret Minimization Perspective](https://mlsanity.com/paper/2409.07819/) (2024)
- [Optimizing Affine Maximizer Auctions via Linear Programming: an Application to Revenue Maximizing Mechanism Design for Zero-Day Exploits Markets](https://mlsanity.com/paper/2006.14185/) (2020)
- [NMA: Neural Multi-slot Auctions with Externalities for Online Advertising](https://mlsanity.com/paper/2205.10018/) (2022)
- [Ranking and Tradeoffs in Sponsored Search Auctions](https://mlsanity.com/paper/1304.7642/) (2013)
- [Joint Auction in the Online Advertising Market](https://mlsanity.com/paper/2408.09885/) (2024)

## Abstract (arXiv)

Online advertising is a vital revenue source for major internet platforms. Recently, joint advertising, which assigns a bundle of two advertisers in an ad slot instead of allocating a single advertiser, has emerged as an effective method for enhancing allocation efficiency and revenue. However, existing mechanisms for joint advertising fail to realize the optimality, as they tend to focus on individual advertisers and overlook bundle structures. This paper identifies an optimal mechanism for joint advertising in a single-slot setting. For multi-slot joint advertising, we propose BundleNet, a novel bundle-based neural network approach specifically designed for joint advertising. Our extensive experiments demonstrate that the mechanisms generated by BundleNet approximate the theoretical analysis results in the single-slot setting and achieve state-of-the-art performance in the multi-slot setting. This significantly increases platform revenue while ensuring approximate dominant strategy incentive compatibility and individual rationality.