Online advertising driven by auctions brings billions of dollars in revenue for social networking services and e-commerce platforms. GSP auctions, which are simple and easy to understand for advertisers, have almost become the benchmark for ad auction mechanisms in the industry. However, most GSP-based industrial practices assume that the user click only relies on the ad itself, which overlook the effect of external items, referred to as externalities. Recently, DNA has attempted to upgrade GSP with deep neural networks and models local externalities to some extent. However, it only considers set-level contexts from auctions and ignores the order and displayed position of ads, which is still suboptimal. Although VCG-based multi-slot auctions (e.g., VCG, WVCG) make it theoretically possible to model global externalities (e.g., the order and positions of ads and so on), they lack an efficient balance of both revenue and social welfare. In this paper, we propose novel auction mechanisms named Neural Multi-slot Auctions (NMA) to tackle the above-mentioned challenges. Specifically, we model the global externalities effectively with a context-aware list-wise prediction module to achieve better performance. We design a list-wise deep rank module to guarantee incentive compatibility in end-to-end learning. Furthermore, we propose an auxiliary loss for social welfare to effectively reduce the decline of social welfare while maximizing revenue. Experiment results on both offline large-scale datasets and online A/B tests demonstrate that NMA obtains higher revenue with balanced social welfare than other existing auction mechanisms (i.e., GSP, DNA, WVCG) in industrial practice, and we have successfully deployed NMA on Meituan food delivery platform.

翻译：由拍卖驱动的在线广告为社交网络服务和电子商务平台带来了数十亿美元的收入。GSP拍卖机制因其对广告主而言简单易懂，几乎已成为行业广告拍卖机制的基准。然而，大多数基于GSP的工业实践假设用户点击仅依赖于广告本身，忽略了外部项目的影响，即外部性。最近，DNA尝试通过深度神经网络升级GSP，并在一定程度上建模了局部外部性，但它仅考虑拍卖中的集合级上下文，忽略了广告的顺序和展示位置，这仍非最优方案。尽管基于VCG的多槽拍卖（例如VCG、WVCG）在理论上可以实现对全局外部性（如广告的顺序和位置等）的建模，但它们缺乏对收入和社交福利的高效平衡。本文提出了一种名为神经多槽拍卖（NMA）的新型拍卖机制，以解决上述挑战。具体而言，我们通过一个上下文感知的列表级预测模块有效建模全局外部性，以实现更优性能。我们设计了一个列表级深度排序模块，以确保端到端学习中的激励兼容性。此外，我们提出了一种针对社交福利的辅助损失函数，以在最大化收入的同时有效减少社交福利的下降。在离线大规模数据集和在线A/B测试上的实验结果表明，与现有工业实践中的其他拍卖机制（即GSP、DNA、WVCG）相比，NMA在平衡社交福利的前提下获得了更高收入，并且我们已成功将NMA部署于美团外卖平台。