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https://github.com/guyulongcs/awesome-deep-reinforcement-learning-papers-for-search-recommendation-advertising

Awesome Deep Reinforcement Learning papers for industrial Search, Recommendation and Advertising.
https://github.com/guyulongcs/awesome-deep-reinforcement-learning-papers-for-search-recommendation-advertising

List: awesome-deep-reinforcement-learning-papers-for-search-recommendation-advertising

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Awesome Deep Reinforcement Learning papers for industrial Search, Recommendation and Advertising.

Host: GitHub
URL: https://github.com/guyulongcs/awesome-deep-reinforcement-learning-papers-for-search-recommendation-advertising
Owner: guyulongcs
Created: 2019-09-28T06:09:31.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2021-06-01T09:55:40.000Z (over 3 years ago)
Last Synced: 2023-11-07T18:29:32.578Z (about 1 year ago)
Homepage:
Size: 9.77 KB
Stars: 187
Watchers: 8
Forks: 32
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

ultimate-awesome - awesome-deep-reinforcement-learning-papers-for-search-recommendation-advertising - Awesome Deep Reinforcement Learning papers for industrial Search, Recommendation and Advertising. . (Other Lists / Monkey C Lists)

README

        # Deep Reinforcement Learning for Recommender Systems 

## Papers

### Recommender Systems: 

    SIGIR 20 Neural Interactive Collaborative Filtering [paper](https://dl.acm.org/doi/pdf/10.1145/3397271.3401181) [code](https://github.com/zoulixin93/NICF)

	KDD 20  Jointly Learning to Recommend and Advertise [paper](https://arxiv.org/pdf/2003.00097.pdf)

	CIKM 20 Whole-Chain Recommendations [paper](https://arxiv.org/pdf/1902.03987.pdf)

	KDD 19 Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems [paper](https://dl.acm.org/citation.cfm?id=3330668) :star:[JD]

    DSFAA 19 Reinforcement Learning to Diversify Top-N Recommendation [paper](https://link.springer.com/chapter/10.1007/978-3-030-18579-4_7) [code]( https://github.com/zoulixin93/FMCTS) :star:[JD]

    KDD 18 Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning [paper](https://dl.acm.org/citation.cfm?id=3219886)  :star:[JD]

    RecSys 18 Deep Reinforcement Learning for Page-wise Recommendations [paper](https://dl.acm.org/citation.cfm?id=3240374)  :star:[JD]

    DRL4KDD Deep Reinforcement Learning for List-wise Recommendations [paper](https://arxiv.org/abs/1801.00209)  :star:[JD]

    Sigweb 19 Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey [paper](https://dl.acm.org/citation.cfm?id=3320500)  :star:[JD]

    Arxiv 19 Model-Based Reinforcement Learning for Whole-Chain Recommendations [paper](https://arxiv.org/abs/1902.03987) :star:[JD]

    Arxiv 19 Simulating User Feedback for Reinforcement Learning Based Recommendations [paper](https://arxiv.org/abs/1906.11462) :star:[JD]

	Arxiv 19 Deep Reinforcement Learning for Online Advertising in Recommender Systems [paper](https://arxiv.org/abs/1909.03602)

### Search Engine:

    KDD 18 Reinforcement Learning to Rank in E-Commerce Search Engine Formalization, Analysis, and Application [paper](https://dl.acm.org/citation.cfm?id=3219846) :star:[Alibaba]

### Advertisement

	Arxiv 19 Deep Reinforcement Learning for Online Advertising in Recommender Systems [paper](https://arxiv.org/pdf/1909.03602.pdf)

### Re-ranking (Top K):

	IJCAI 19 Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology [paper](https://www.cs.toronto.edu/~cebly/Papers/SlateQ_IJCAI_2019.pdf) [arxiv](https://arxiv.org/abs/1905.12767)  :star:[Google]

	Arixv 19 Seq2Slate: Re-ranking and Slate Optimization with RNNs  [paper](https://arxiv.org/abs/1810.02019) :star:[Google]

    KDD 19 Exact-K Recommendation via Maximal Clique Optimization [paper](https://dl.acm.org/citation.cfm?id=3292500.3330832) :star:[Alibaba]

	WWW 19 Value-aware Recommendation based on Reinforcement Profit Maximization [paper](https://dl.acm.org/citation.cfm?id=3313404) [code](https://github.com/rec-agent/rec-rl ) [Dataset](https://drive.google.com/file/d/14OtIC8eiDkzoWCTtaUZHcb7eB-bUmtTT/view) :star:[Alibaba]

### Bandit:

    WWW 10 A Contextual-Bandit Approach to Personalized News Article Recommendation [paper](https://dl.acm.org/citation.cfm?id=1772758)

    KDD 16 Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit [paper](https://dl.acm.org/citation.cfm?id=2939878)

    CIKM 17 Returning is Believing Optimizing Long-term User Engagement in Recommender Systems

    ICLR 18 Deep Learning with Logged Bandit Feedback [paper](https://dl.acm.org/citation.cfm?id=3133025)

    Recsys 18 Explore, Exploit, and Explain Personalizing Explainable Recommendations with Bandits [paper](https://dl.acm.org/citation.cfm?id=3240354)

### Hierarchical RL

    AAAI19 Hierarchical Reinforcement Learning for Course Recommendation in MOOCs [paper](https://xiaojingzi.github.io/publications/AAAI19-zhang-et-al-HRL.pdf)

    WWW 19 Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning [paper](https://dl.acm.org/citation.cfm?id=3313455) :star:[Alibaba]

### DQN:

    WWW 18 DRN: A Deep Reinforcement Learning Framework for News Recommendation [paper](https://dl.acm.org/citation.cfm?id=3185994) :star:[Microsoft]

    KDD 18 Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation [paper](https://dl.acm.org/citation.cfm?id=3220122)  :star:[Alibaba]

    ICML 19 Off-Policy Deep Reinforcement Learning without Exploration [paper](http://proceedings.mlr.press/v97/fujimoto19a/fujimoto19a.pdf)

### Policy Gradient:

    WSDM 19 Top-K Off-Policy Correction for a REINFORCE Recommender System [paper](https://dl.acm.org/citation.cfm?id=3290999) :star:[Google]

    NIPS 17 Off-policy evaluation for slate recommendation [paper](http://papers.nips.cc/paper/6954-off-policy-evaluation-for-slate-recommendation.pdf)

    ICML 19 Safe Policy Improvement with Baseline Bootstrapping [paper](http://proceedings.mlr.press/v97/laroche19a/laroche19a.pdf)

    WWW 19 Policy Gradients for Contextual Recommendations [paper](https://dl.acm.org/citation.cfm?id=3313616)

    AAAI 19 Large-scale Interactive Recommendation with Tree-structured Policy Gradient [paper](https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/4204)

### Actor-Critic:

    Arxiv 15 Deep Reinforcement Learning in Large Discrete Action Spaces [paper](https://arxiv.org/abs/1512.07679) [code](https://github.com/jimkon/Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces)

    Arxiv 18 Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling [paper](https://arxiv.org/abs/1810.12027)

    KDD 18 Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation [paper](https://dl.acm.org/citation.cfm?id=3219961)

### Multi-agent:

    WWW 18 Learning to Collaborate Multi-Scenario Ranking via Multi-Agent Reinforcement Learning [paper](https://dl.acm.org/citation.cfm?id=3186165) 

:star:[Alibaba]

### Offline:

    WSDM 19 Offline Evaluation to Make Decisions About Playlist Recommendation Algorithms [paper](https://dl.acm.org/citation.cfm?id=3291027)

     KDD 19 Off-policy Learning for Multiple Loggers [paper](https://dl.acm.org/citation.cfm?id=3330864)

### Explainable:

    ICDM 18 A Reinforcement Learning Framework for Explainable Recommendation [paper](https://www.microsoft.com/en-us/research/uploads/prod/2018/08/main.pdf)

    SIGIR 19 Reinforcement Knowledge Graph Reasoning for Explainable Recommendation [paper](https://dl.acm.org/citation.cfm?id=3331203)

### Simulation:

    ICML 19 Generative Adversarial User Model for Reinforcement Learning Based Recommendation System [paper](http://proceedings.mlr.press/v97/chen19f.html)

### Research Scientists:

[Jun Wang](http://www0.cs.ucl.ac.uk/staff/Jun.Wang/), [Jun Xu](https://scholar.google.com/citations?user=su14mcEAAAAJ&hl=en), [Weinan Zhang](http://wnzhang.net/), [Xiangyu Zhao](https://www.cse.msu.edu/~zhaoxi35/), [Lixin Zou](https://scholar.google.com/citations?user=J8tHYjIAAAAJ&hl=zh-CN)