RL4Rec_papers

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# RL4Rec_papers

This list is one of the most complete collection for papers about RL based recommender system, and  it especially focuses on actual engineering experience from companies .

I do believe a RL based recommender system is trustworthy **if and only if the online A/B tests have been conducted** (They usually comes from companies), so I marked these papers' title in bold.

Notes: This repo is similar to [@cszhangzhen's repo](https://github.com/cszhangzhen/DRL4Recsys),  but pays more attention to the works already tested in real-world scenario.

## Selected Papers (Must Read)

1. DRN: A Deep Reinforcement Learning Framework for News Recommendation [paper](http://www.personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf)
  2. Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning [paper](https://arxiv.org/abs/1802.06501)
  3. Top-K Off-Policy Correction for a REINFORCE Recommender System [paper](https://arxiv.org/pdf/1812.02353.pdf)
  4. Deep Reinforcement Learning for Search, Recommendation, and Online Advertising A Survey [paper](https://arxiv.org/abs/1812.07127)

## Technical Blog(Chinese)
1. [阿里巴巴-脱胎换骨的生成模式：强化学习重排](https://mp.weixin.qq.com/s/u44neSzF2Iqyv-i1Fc4lsQ)
2. [滴滴出行-基于强化学习的 Contextual Bandits 算法在推荐场景中的应用](https://mp.weixin.qq.com/s/6zzOU_hrtAGFPnDz2KuqVg)
3. [阿里巴巴-强化学习在阿里的技术演进和业务创新](http://techforum-img.cn-hangzhou.oss-pub.aliyun-inc.com/1517812754285/reinforcement_learning.pdf)
4. [美团-强化学习在美团“猜你喜欢”的实践](https://tech.meituan.com/2018/11/15/reinforcement-learning-in-mt-recommend-system.html)

## 2020

### SIGIR 2020
  1. KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation
  2. Self-Supervised Reinforcement Learning for Recommender Systems
  3. Reinforcement Learning to Rank with Pairwise Policy Gradient
  4. **MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations** [paper](https://arxiv.org/pdf/1903.09374.pdf)
  5. **Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning**
  6. Adversarial Attack and Detection on Reinforcement Learning based Recommendation System
  7. Reinforcement Learning based Recommendation with Graph Convolutional Q-network
  8. Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval [paper](https://arxiv.org/pdf/2006.03185.pdf)
  9. A Reinforcement Learning Framework for Relevance Feedback
  10. Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation

### WSDM 2020
  1. End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding [paper](https://dl.acm.org/doi/pdf/10.1145/3336191.3371858)

2. **Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation**

### WWW 2020
  1. [Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation](https://arxiv.org/pdf/2004.01136.pdf)

### Preprint paper
  1. **Validation Set Evaluation can be Wrong: An Evaluator-Generator Approach for Maximizing Online Performance of Ranking in E-commerce**  [paper](https://arxiv.org/abs/2003.11941)

## 2019
### SIGKDD 2019
  1. **Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems** [paper](https://arxiv.org/abs/1902.05570)
  2. **Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation** [paper](https://www.kdd.org/kdd2019/accepted-papers/view/reinforcement-learning-to-optimize-long-term-user-engagement-in-recommender)
  3. Exact-K Recommendation via Maximal Clique Optimization[paper](https://arxiv.org/pdf/1905.07089.pdf)

### WSDM 2019
  1. Top-K Off-Policy Correction for a REINFORCE Recommender System[paper](https://arxiv.org/pdf/1812.02353.pdf)
  2. **Model-Based Reinforcement Learning for Whole-Chain Recommendations**[paper](https://arxiv.org/pdf/1902.03987.pdf)

### ICML 2019
  1. Generative Adversarial User Model for Reinforcement Learning Based Recommendation System [paper](http://proceedings.mlr.press/v97/chen19f/chen19f.pdf)
### AAAI 2019
  1. Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning [paper](https://arxiv.org/abs/1805.10000)
  2. Hierarchical Reinforcement Learning for Course Recommendation in MOOCs [paper](https://xiaojingzi.github.io/publications/AAAI19-zhang-et-al-HRL.pdf)
  3. Large-scale Interactive Recommendation with Tree-structured Policy Gradient [paper](https://arxiv.org/abs/1811.05869), [code](https://github.com/chenhaokun/TPGR)

### WWW 2019
  1. Policy Gradients for Contextual Recommendations [paper](https://arxiv.org/pdf/1802.04162.pdf)

### CIKM 2019
### SIGIR 2019

### NeurIPS 2019
  1. A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation [paper](http://papers.nips.cc/paper/9257-a-model-based-reinforcement-learning-with-adversarial-training-for-online-recommendation.pdf)
  2. Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning [paper](http://people.ee.duke.edu/~lcarin/Ruiyi_NeurIPS2019.pdf)

### Preprint paper
  1. Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling [paper](https://arxiv.org/pdf/1810.12027.pdf)
## 2018
### KDD 2018
  1. Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation [paper](https://dl.acm.org/doi/10.1145/3219819.3220122)

### WWW 2018
  1. **DRN: A Deep Reinforcement Learning Framework for News Recommendation** [paper](http://www.personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf)
  2. Learning to Collaborate-Multi-Scenario Ranking via Multi-Agent Reinforcement Learning [paper](https://arxiv.org/abs/1809.06260)
  3. Reinforcement Mechanism Design for e-commerce [paper](https://arxiv.org/pdf/1708.07607.pdf)

### RecSys 2018
  1. **Deep Reinforcement Learning for Page-wise Recommendations**

### Preprint paper
  1. Deep Reinforcement Learning for List-wise Recommendations

RL4Rec_papers

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