# Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed

arXiv: https://arxiv.org/abs/2401.01656 · HTML: https://mlsanity.com/paper/2401.01656/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 3 Jan 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Deep Position-wise Interaction Network for CTR Prediction](https://mlsanity.com/paper/2106.05482/) (2021)
- [Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation](https://mlsanity.com/paper/2204.00377/) (2022)
- [Hierarchically Constrained Adaptive Ad Exposure in Feeds](https://mlsanity.com/paper/2205.15759/) (2022)
- [Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation](https://mlsanity.com/paper/2204.11589/) (2022)
- [NMA: Neural Multi-slot Auctions with Externalities for Online Advertising](https://mlsanity.com/paper/2205.10018/) (2022)
- [Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks](https://mlsanity.com/paper/2204.00888/) (2022)
- [Ad auctions and cascade model: GSP inefficiency and algorithms](https://mlsanity.com/paper/1511.07397/) (2015)
- [Multi-channel Integrated Recommendation with Exposure Constraints](https://mlsanity.com/paper/2305.12319/) (2023)
- [Optimally Integrating Ad Auction into E-Commerce Platforms](https://mlsanity.com/paper/2007.09359/) (2020)
- [MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed](https://mlsanity.com/paper/2304.09087/) (2023)

## Built on by (most cited first, 6 of 6)

- [Distributed General-Purpose Agent Networks: Architecture, Key Mechanisms, and Prototypes](https://mlsanity.com/paper/2606.17368/) (2026)
- [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)
- [Open Problems in Differentiable Social Choice: Learning Mechanisms, Decisions, and Alignment](https://mlsanity.com/paper/2602.03003/) (2026)
- [Enhancing Affine Maximizer Auctions with Correlation-Aware Payment](https://mlsanity.com/paper/2602.09455/) (2026)
- [Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation](https://mlsanity.com/paper/2605.26424/) (2026)

## Abstract (arXiv)

E-commerce platforms usually present an ordered list, mixed with several organic items and an advertisement, in response to each user's page view request. This list, the outcome of ad auction and allocation processes, directly impacts the platform's ad revenue and gross merchandise volume (GMV). Specifically, the ad auction determines which ad is displayed and the corresponding payment, while the ad allocation decides the display positions of the advertisement and organic items. The prevalent methods of segregating the ad auction and allocation into two distinct stages face two problems: 1) Ad auction does not consider externalities, such as the influence of actual display position and context on ad Click-Through Rate (CTR); 2) The ad allocation, which utilizes the auction-winning ad's payment to determine the display position dynamically, fails to maintain incentive compatibility (IC) for the advertisement. For instance, in the auction stage employing the traditional Generalized Second Price (GSP) , even if the winning ad increases its bid, its payment remains unchanged. This implies that the advertisement cannot secure a better position and thus loses the opportunity to achieve higher utility in the subsequent ad allocation stage. Previous research often focused on one of the two stages, neglecting the two-stage problem, which may result in suboptimal outcomes...