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：基于拍卖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（广义第二价格）拍卖因简单易懂而几乎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试图通过深度神经网络升级GSP，并在一定程度上建模局部外部性，但其仅考虑拍卖中的集合级上下文，忽略广告的排序和展示位置，仍非最优方法。尽管基于VCG的多槽位拍卖（如VCG、WVCG）在理论上可以建模全局外部性（如广告顺序、位置等），但其缺乏对收入和社会福利的高效平衡。本文提出名为神经多槽位拍卖（NMA）的新型拍卖机制，以解决上述挑战。具体而言，我们利用上下文感知的列表级预测模块有效建模全局外部性，实现更优性能；设计列表级深度排序模块，确保端到端学习中的激励相容性；进一步提出社会福利辅助损失，在最大化收入的同时有效降低社会福利损失。离线大规模数据集实验和在线A/B测试结果表明，与工业实践中其他现有拍卖机制（如GSP、DNA、WVCG）相比，NMA在保持社会福利平衡的前提下获得了更高收入，且我们已成功将NMA部署于美团外卖平台。