# Deep Reinforcement Learning for Page-wise Recommendations

arXiv: https://arxiv.org/abs/1805.02343 · HTML: https://mlsanity.com/paper/1805.02343/

## Key facts

- Cited by 100 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 30 of 100)

- [Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations](https://mlsanity.com/paper/2402.17152/) (2024)
- [Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning](https://mlsanity.com/paper/2505.14069/) (2025)
- [A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost](https://mlsanity.com/paper/2101.00494/) (2021)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning](https://mlsanity.com/paper/2110.04471/) (2021)
- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments](https://mlsanity.com/paper/2102.08492/) (2021)
- [Multi-Task Recommendations with Reinforcement Learning](https://mlsanity.com/paper/2302.03328/) (2023)
- [Large Language Models are Learnable Planners for Long-Term Recommendation](https://mlsanity.com/paper/2403.00843/) (2024)
- [User Tampering in Reinforcement Learning Recommender Systems](https://mlsanity.com/paper/2109.04083/) (2021)
- [Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems](https://mlsanity.com/paper/2208.04560/) (2022)
- [Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective](https://mlsanity.com/paper/2101.02634/) (2021)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions](https://mlsanity.com/paper/2109.03540/) (2021)
- [Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning](https://mlsanity.com/paper/2208.13663/) (2022)
- [A General Offline Reinforcement Learning Framework for Interactive Recommendation](https://mlsanity.com/paper/2310.00678/) (2023)
- [Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM](https://mlsanity.com/paper/2312.15450/) (2023)
- [Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation](https://mlsanity.com/paper/2109.08331/) (2021)
- [Variational Quantum Soft Actor-Critic](https://mlsanity.com/paper/2112.11921/) (2021)
- [Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2504.18383/) (2025)
- [Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities](https://mlsanity.com/paper/2111.11509/) (2021)
- [Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning](https://mlsanity.com/paper/2304.07920/) (2023)
- [Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval](https://mlsanity.com/paper/2510.08935/) (2025)
- [On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems](https://mlsanity.com/paper/2308.11336/) (2023)
- [Model-free Policy Learning with Reward Gradients](https://mlsanity.com/paper/2103.05147/) (2021)
- [Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation](https://mlsanity.com/paper/2108.09141/) (2021)
- [Local Policy Improvement for Recommender Systems](https://mlsanity.com/paper/2212.11431/) (2022)
- [Multi-agent Attacks for Black-box Social Recommendations](https://mlsanity.com/paper/2311.07127/) (2023)
- [EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems](https://mlsanity.com/paper/2402.15164/) (2024)

## Abstract (arXiv)

Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is -- users are recommended a page of items and provide feedback; and then the system recommends a new page of items. To effectively capture such interaction for recommendations, we need to solve two key problems -- (1) how to update recommending strategy according to user's textitreal-time feedback, and 2) how to generate a page of items with proper display, which pose tremendous challenges to traditional recommender systems. In this paper, we study the problem of page-wise recommendations aiming to address aforementioned two challenges simultaneously. In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.