# Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising

arXiv: https://arxiv.org/abs/2106.03593 · HTML: https://mlsanity.com/paper/2106.03593/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Model-Ensemble Trust-Region Policy Optimization](https://mlsanity.com/paper/1802.10592/) (2018)
- [Towards a Neural Statistician](https://mlsanity.com/paper/1606.02185/) (2016)
- [Stochastic Optimization of Sorting Networks via Continuous Relaxations](https://mlsanity.com/paper/1903.08850/) (2019)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [FSPool: Learning Set Representations with Featurewise Sort Pooling](https://mlsanity.com/paper/1906.02795/) (2019)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Complexity of Mechanism Design](https://mlsanity.com/paper/1408.1486/) (2014)
- [Automated Mechanism Design via Neural Networks](https://mlsanity.com/paper/1805.03382/) (2018)
- [Auction learning as a two-player game](https://mlsanity.com/paper/2006.05684/) (2020)
- [Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising](https://mlsanity.com/paper/2012.02930/) (2020)
- [GSP - The Cinderella of Mechanism Design](https://mlsanity.com/paper/1701.05946/) (2017)
- [Online Learning for Measuring Incentive Compatibility in Ad Auctions](https://mlsanity.com/paper/1901.06808/) (2019)
- [Optimising Trade-offs Among Stakeholders in Ad Auctions](https://mlsanity.com/paper/1404.5127/) (2014)

## Built on by (most cited first, 30 of 31)

- [Sustainable Online Reinforcement Learning for Auto-bidding](https://mlsanity.com/paper/2210.07006/) (2022)
- [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)
- [A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising](https://mlsanity.com/paper/2106.06224/) (2021)
- [A Context-Integrated Transformer-Based Neural Network for Auction Design](https://mlsanity.com/paper/2201.12489/) (2022)
- [Learning Revenue-Maximizing Auctions With Differentiable Matching](https://mlsanity.com/paper/2106.07877/) (2021)
- [Ad-load Balancing via Off-policy Learning in a Content Marketplace](https://mlsanity.com/paper/2309.11518/) (2023)
- [LLM-Auction: Generative Auction towards LLM-Native Advertising](https://mlsanity.com/paper/2512.10551/) (2025)
- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [Hierarchically Constrained Adaptive Ad Exposure in Feeds](https://mlsanity.com/paper/2205.15759/) (2022)
- [Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed](https://mlsanity.com/paper/2401.01656/) (2024)
- [NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems](https://mlsanity.com/paper/2502.06097/) (2025)
- [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025)
- [NMA: Neural Multi-slot Auctions with Externalities for Online Advertising](https://mlsanity.com/paper/2205.10018/) (2022)
- [InfoBid: A Simulation Framework for Studying Information Disclosure in Auctions with Large Language Model-based Agents](https://mlsanity.com/paper/2503.22726/) (2025)
- [Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising](https://mlsanity.com/paper/2110.13587/) (2021)
- [Exploiting hidden structures in non-convex games for convergence to Nash equilibrium](https://mlsanity.com/paper/2312.16609/) (2023)
- [An Auction-based Marketplace for Model Trading in Federated Learning](https://mlsanity.com/paper/2402.01802/) (2024)
- [Automated Deterministic Auction Design with Objective Decomposition](https://mlsanity.com/paper/2402.11904/) (2024)
- [LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots](https://mlsanity.com/paper/2605.16474/) (2026)
- [A Survey of Online Auction Mechanism Design Using Deep Learning Approaches](https://mlsanity.com/paper/2110.06880/) (2021)
- [Bid2X: Revealing Dynamics of Bidding Environment in Online Advertising from A Foundation Model Lens](https://mlsanity.com/paper/2510.23410/) (2025)
- [Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design](https://mlsanity.com/paper/2601.13489/) (2026)
- [ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation](https://mlsanity.com/paper/2605.28293/) (2026)
- [Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising](https://mlsanity.com/paper/2304.00999/) (2023)
- [EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising](https://mlsanity.com/paper/2305.06158/) (2023)
- [STUDY: Socially Aware Temporally Causal Decoder Recommender Systems](https://mlsanity.com/paper/2306.07946/) (2023)
- [Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications](https://mlsanity.com/paper/2401.05683/) (2024)
- [Optimal Auction Design in the Joint Advertising](https://mlsanity.com/paper/2507.07418/) (2025)
- [Statistical Learning from Attribution Sets](https://mlsanity.com/paper/2602.06276/) (2026)

## Abstract (arXiv)

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare). Recently, data-driven auctions, learned directly from auction outcomes to optimize multiple performance metrics, have attracted increasing research interests. However, the procedure of auction mechanisms involves various discrete calculation operations, making it challenging to be compatible with continuous optimization pipelines in machine learning. In this paper, we design underlineDeep underlineNeural underlineAuctions (DNAs) to enable end-to-end auction learning by proposing a differentiable model to relax the discrete sorting operation, a key component in auctions. We optimize the performance metrics by developing deep models to efficiently extract contexts from auctions, providing rich features for auction design. We further integrate the game theoretical conditions within the model design, to guarantee the stability of the auctions. DNAs have been successfully deployed in the e-commerce advertising system at Taobao. Experimental evaluation results on both large-scale data set as well as online A/B test demonstrated that DNAs significantly outperformed other mechanisms widely adopted in industry.