提出新机制提升联合广告收益，兼顾效率与激励相容。
Optimal Auction Design in the Joint Advertising
- 设计基于捆绑结构的神经网络机制，优化多广告位分配
- 实验显示收益显著提升，逼近理论最优解
- 适合平台方优化广告拍卖策略，提升收入
在线广告是主要互联网平台的重要收入来源。近期出现的联合广告机制，将两个广告商打包分配至一个广告位，以提升分配效率和收益。然而现有方法多关注单个广告商，忽视捆绑结构，难以实现最优。本文在单广告位场景下提出最优机制；针对多广告位，提出专用神经网络模型 BundleNet。大量实验表明，BundleNet 生成的机制在单广告位下接近理论最优，在多广告位下达到当前最佳性能，显著提升平台收入，同时保证近似主导策略激励相容与个体理性。
原文摘要 · Abstract (English)
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 \textbf{BundleNet}, a novel bundle-based neural network approach specifically designed for joint advertising. Our extensive experiments demonstrate that the mechanisms generated by \textbf{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.
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