# Hierarchically Constrained Adaptive Ad Exposure in Feeds

arXiv: https://arxiv.org/abs/2205.15759 · HTML: https://mlsanity.com/paper/2205.15759/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 11 of 11)

- [Pareto Multi-Task Learning](https://mlsanity.com/paper/1912.12854/) (2019)
- [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.09756/) (2018)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (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)
- [Jointly Learning to Recommend and Advertise](https://mlsanity.com/paper/2003.00097/) (2020)
- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising](https://mlsanity.com/paper/2006.16312/) (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)
- [Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds](https://mlsanity.com/paper/1908.06698/) (2019)

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

- [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)
- [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025)
- [Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning](https://mlsanity.com/paper/2501.05591/) (2025)
- [One Model, Two Markets: Bid-Aware Generative Recommendation](https://mlsanity.com/paper/2603.22231/) (2026)
- [Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation](https://mlsanity.com/paper/2605.26424/) (2026)

## Abstract (arXiv)

A contemporary feed application usually provides blended results of organic items and sponsored items~(ads) to users. Conventionally, ads are exposed at fixed positions. Such a static exposure strategy is inefficient due to ignoring users' personalized preferences towards ads. To this end, adaptive ad exposure has become an appealing strategy to boost the overall performance of the feed. However, existing approaches to implementing the adaptive ad exposure still suffer from several limitations: 1) they usually fall into sub-optimal solutions because of only focusing on request-level optimization without consideration of the long-term application-level performance and constraints, 2) they neglect the necessity of keeping the game-theoretical properties of ad auctions, which may lead to anarchy in bidding, and 3) they can hardly be deployed in large-scale applications due to high computational complexity. In this paper, we focus on long-term performance optimization under hierarchical constraints in feeds and formulate the adaptive ad exposure as a Dynamic Knapsack Problem. We propose an effective approach: Hierarchically Constrained Adaptive Ad Exposure~(HCA2E). We present that HCA2E possesses desired game-theoretical properties, computational efficiency, and performance robustness. Comprehensive offline and online experiments on a leading e-commerce application demonstrate the significant performance superiority of HCA2E over representative baselines. HCA2E has also been deployed on this application to serve millions of daily users.