# Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation

arXiv: https://arxiv.org/abs/2204.00377 · HTML: https://mlsanity.com/paper/2204.00377/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [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)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [Jointly Learning to Recommend and Advertise](https://mlsanity.com/paper/2003.00097/) (2020)
- [Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1809.06260/) (2018)
- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [A Non-sequential Approach to Deep User Interest Model for CTR Prediction](https://mlsanity.com/paper/2104.06312/) (2021)

## Built on by (most cited first, 8 of 8)

- [Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed](https://mlsanity.com/paper/2401.01656/) (2024)
- [Decision-Making Context Interaction Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2301.12402/) (2023)
- [Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks](https://mlsanity.com/paper/2204.00888/) (2022)
- [Ads Supply Personalization via Doubly Robust Learning](https://mlsanity.com/paper/2410.12799/) (2024)
- [Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning](https://mlsanity.com/paper/2501.05591/) (2025)
- [MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed](https://mlsanity.com/paper/2304.09087/) (2023)
- [WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data](https://mlsanity.com/paper/2312.14129/) (2023)
- [Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest](https://mlsanity.com/paper/2509.05292/) (2025)

## Abstract (arXiv)

A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, modeling user preference with historical behavior is essential in recommendation and advertising (e.g., CTR prediction and ads allocation). Most previous works for user behavior modeling only model user's historical point-level positive feedback (i.e., click), which neglect the page-level information of feedback and other types of feedback. To this end, we propose Deep Page-level Interest Network (DPIN) to model the page-level user preference and exploit multiple types of feedback. Specifically, we introduce four different types of page-level feedback as input, and capture user preference for item arrangement under different receptive fields through the multi-channel interaction module. Through extensive offline and online experiments on Meituan food delivery platform, we demonstrate that DPIN can effectively model the page-level user preference and increase the revenue for the platform.