# NMA: Neural Multi-slot Auctions with Externalities for Online Advertising

arXiv: https://arxiv.org/abs/2205.10018 · HTML: https://mlsanity.com/paper/2205.10018/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Stochastic Optimization of Sorting Networks via Continuous Relaxations](https://mlsanity.com/paper/1903.08850/) (2019)
- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Disentangling Long and Short-Term Interests for Recommendation](https://mlsanity.com/paper/2202.13090/) (2022)
- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising](https://mlsanity.com/paper/2012.02930/) (2020)
- [Deep Position-wise Interaction Network for CTR Prediction](https://mlsanity.com/paper/2106.05482/) (2021)
- [Optimising Trade-offs Among Stakeholders in Ad Auctions](https://mlsanity.com/paper/1404.5127/) (2014)
- [On Designing a Two-stage Auction for Online Advertising](https://mlsanity.com/paper/2111.05555/) (2021)
- [Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data](https://mlsanity.com/paper/2202.03097/) (2022)
- [Truthful Learning Mechanisms for Multi-Slot Sponsored Search Auctions with Externalities](https://mlsanity.com/paper/1405.2484/) (2014)
- [Ad auctions and cascade model: GSP inefficiency and algorithms](https://mlsanity.com/paper/1511.07397/) (2015)
- [Towards Better Models of Externalities in Sponsored Search Auctions](https://mlsanity.com/paper/1604.04095/) (2016)

## Built on by (most cited first, 4 of 4)

- [PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://mlsanity.com/paper/2302.03487/) (2023)
- [Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed](https://mlsanity.com/paper/2401.01656/) (2024)
- [EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising](https://mlsanity.com/paper/2305.06158/) (2023)
- [Optimal Auction Design in the Joint Advertising](https://mlsanity.com/paper/2507.07418/) (2025)

## Abstract (arXiv)

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.