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<a class="toc" id="table-of-contents"></a>
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# Awesome Adversarial Learning on Recommender System (Updating)
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[](https://github.com/sindresorhus/awesome)
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[](http://makeapullrequest.com)
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### Contents
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+ [Attack](#1)
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+ [2020](#1-1)
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+ [2019](#1-2)
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+ [2018](#1-3)
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+ [2017](#1-4)
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+ [2016](#1-5)
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+ [Defense](#2)
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+ [2020](#2-1)
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+ [2019](#2-2)
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+ [2018](#2-3)
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+ [2017](#2-4)
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+ [2016](#2-5)
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+ [Survey](#3)
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+ [Resource](#4)
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<a class="toc" id ="1"></a>
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# Attack
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[🔙](#table-of-contents)
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<a class="toc" id ="1-1"></a>
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## 2020
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+ **Data Poisoning Attacks on Neighborhood-based Recommender Systems**, *ETT*, [[📝Paper]](https://arxiv.org/abs/1912.04109)
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+ **Attacking Black-box Recommendations via Copying Cross-domain User Profiles**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2005.08147)
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+ **Attacking Black-box Recommendations via Copying Cross-domain User Profiles**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2005.08147)
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+ **Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2006.07934)
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+ **Adversarial Attacks on Linear Contextual Bandits**, *Arxiv*, [[📝Paper]](https://arxiv.org/pdf/2002.03839)
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+ **Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2006.01888), [[🔥Code]](https://github.com/liuzrcc/AIP)
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+ **Influence Function based Data Poisoning Attacks to Top-N Recommender Systems**, *WWW*, [[📝Paper]](https://arxiv.org/abs/2002.08025)
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+ **TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems**, *Dependable and Secure Machine Learning (DSML)*, [[📝Paper]](http://sisinflab.poliba.it/publications/2020/DMM20/PID6442119.pdf), [[🔥Code]](https://github.com/sisinflab/TAaMR)
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<a class="toc" id ="1-2"></a>
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## 2019
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+ **Adversarial Attacks on an Oblivious Recommender**, *RecSys*, [[📝Paper]](https://dl.acm.org/doi/10.1145/3298689.3347031)
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+ **Targeted Poisoning Attacks on Social Recommender Systems**, *IEEE Global Communications Conference (GLOBECOM)*, [[📝Paper]](https://ieeexplore.ieee.org/document/9013539)
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<a class="toc" id ="1-3"></a>
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## 2018
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+ **Poisoning attacks to graph-based recommender systems**, *Annual Computer Security Applications Conference (ACSAC)*, [[📝Paper]](https://arxiv.org/abs/1809.04127), [[🔥Code]](https://github.com/alanefl/graph-based-recommender-attacks)
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<a class="toc" id ="1-4"></a>
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## 2017
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+ **Fake Co-visitation Injection Attacks to Recommender Systems**, *NDSS*, [[📝Paper]](http://people.duke.edu/~zg70/papers/ndss17-attackRS.pdf)
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+ **Hybrid attacks on model-based social recommender systems**, *Physica A: Statistical Mechanics and its Applications*, [[📝Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0378437117303436)
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<a class="toc" id ="1-5"></a>
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## 2016
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+ **Data Poisoning Attacks on Factorization-Based Collaborative Filtering**, *NIPS*, [[📝Paper]](https://arxiv.org/abs/1608.08182), [[🔥Code]](https://github.com/fuying-wang/Data-poisoning-attacks-on-factorization-based-collaborative-filtering)
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+ **Segment-Focused Shilling Attacks against Recommendation Algorithms in Binary Ratings-based Recommender Systems**, *International Journal of Hybrid Information Technology*, [[📝Paper]](https://www.semanticscholar.org/paper/Segment-Focused-Shilling-Attacks-against-Algorithms-Zhang/5c7e96dcaf253f37904f91fdb6fdd6f486dba134)
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+ **Shilling attack detection in collaborative filtering recommender system by PCA detection and perturbation**, *International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)*, [[📝Paper]](https://ieeexplore.ieee.org/document/7731644)
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+ **Shilling attack models in recommender system**, *International Conference on Inventive Computation Technologies (ICICT)*, [[📝Paper]](https://ieeexplore.ieee.org/document/7824865)
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<a class="toc" id ="2"></a>
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# Defense
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[🔙](#table-of-contents)
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<a class="toc" id ="2-1"></a>
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+ **Abstract Interpretation based Robustness Certification for Graph Convolutional Networks**, *ECAI*, [[📝Paper]](http://ecai2020.eu/papers/31_paper.pdf)
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## 2020
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+ **A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2004.14734)
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+ **Adversarial Collaborative Auto-encoder for Top-N Recommendation**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/1808.05361)
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+ **Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems**, *Arxiv*, [[📝Paper]](https://arxiv.org/abs/2006.07934)
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+ **Adversarial Learning to Compare: Self-Attentive Prospective Customer Recommendation in Location based Social Networks**, *WSDM*, [[📝Paper]](https://dl.acm.org/doi/abs/10.1145/3336191.3371841)
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+ **Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines**, *SIGIR*, [[📝Paper]](http://jiyang3.web.engr.illinois.edu/files/fm-rt.pdf)
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+ **Directional Adversarial Training for Recommender Systems**, *ECAI*, [[📝Paper]](http://ecai2020.eu/papers/300_paper.pdf)
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<a class="toc" id ="2-2"></a>
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## 2019
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+ **Adversarial Training Towards Robust Multimedia Recommender System**, *TKDE*, [[📝Paper]](https://graphreason.github.io/papers/35.pdf), [[🔥Code]](https://github.com/duxy-me/AMR)
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+ **Adversarial Collaborative Neural Network for Robust Recommendation**, *SIGIR*, [[📝Paper]](https://www.researchgate.net/publication/332861957_Adversarial_Collaborative_Neural_Network_for_Robust_Recommendation)
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+ **Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation**, *SIGIR*, [[📝Paper]](http://web.cs.wpi.edu/~kmlee/pubs/tran19sigir.pdf), [[🔥Code]](https://github.com/thanhdtran/MASR)
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+ **Adversarial tensor factorization for context-aware recommendation**, *RecSys*, [[📝Paper]](https://dl.acm.org/doi/10.1145/3298689.3346987), [[🔥Code]]
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+ **Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System**, *IEEE Access*, [[📝Paper]](https://ieeexplore.ieee.org/document/8946325)
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<a class="toc" id ="2-3"></a>
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## 2018
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+ **Adversarial Personalized Ranking for Recommendation**, *SIGIR*, [[📝Paper]](https://dl.acm.org/citation.cfm?id=3209981), [[🔥Code]](https://github.com/hexiangnan/adversarial_personalized_ranking)
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+ **A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network**, *The Computer Journal*, [[📝Paper]](https://academic.oup.com/comjnl/article-abstract/61/7/949/4835634)
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+ **Adversarial Sampling and Training for Semi-Supervised Information Retrieval**, *WWW*, [[📝Paper]](https://arxiv.org/abs/1506.05752)
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+ **Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks**, *IEEE Transactions on Multimedia*, [[📝Paper]](https://ieeexplore.ieee.org/document/8576563)
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+ **Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning**, *WSDM*, [[📝Paper]](https://arxiv.org/abs/1911.09872)
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<a class="toc" id ="2-4"></a>
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## 2017
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+ **Detecting Abnormal Profiles in Collaborative Filtering Recommender Systems**, *Journal of Intelligent Information Systems*, [[📝Paper]](https://link.springer.com/article/10.1007/s10844-016-0424-5)
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+ **Detection of Profile Injection Attacks in Social Recommender Systems Using Outlier Analysis**, *IEEE Big Data*, [[📝Paper]](http://www.cs.ucf.edu/~anahita/08258235.pdf)
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+ **Prevention of shilling attack in recommender systems using discrete wavelet transform and support vector machine**, *Eighth International Conference on Advanced Computing (ICoAC)*, [[📝Paper]](https://ieeexplore.ieee.org/document/7951753)
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<a class="toc" id ="2-5"></a>
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## 2016
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+ **Discovering shilling groups in a real e-commerce platform**, *Online Information Review*, [[📝Paper]](https://www.emerald.com/insight/content/doi/10.1108/OIR-03-2015-0073/full/html)
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<a class="toc" id ="3"></a>
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# Survey
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[🔙](#table-of-contents)
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+ **Adversarial Machine Learning in Recommender Systems: State of the art and Challenges**, *Arxiv2020*, [[📝Paper]]((https://arxiv.org/abs/2005.10322))
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+ **A Survey of Adversarial Learning on Graphs**, *Arxiv2020*, [[📝Paper]](https://arxiv.org/abs/2003.05730)
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+ **Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study**, *Arxiv2020*, [[📝Paper]](https://arxiv.org/abs/2003.00653)
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+ **Adversarial Attacks and Defenses in Images, Graphs and Text: A Review**, *Arxiv2019*, [[📝Paper]](https://arxiv.org/abs/1909.08072)
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+ **Adversarial Attack and Defense on Graph Data: A Survey**, *Arxiv2018*, [[📝Paper]](https://arxiv.org/abs/1812.10528)
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<a class="toc" id ="4"></a>
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# Resource
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[🔙](#table-of-contents)
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+ Awesome Graph Adversarial Learning, [[:octocat:Link]](https://github.com/gitgiter/Graph-Adversarial-Learning)
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+ Awesome Graph Attack and Defense Papers, [[:octocat:Link]](https://github.com/ChandlerBang/awesome-graph-attack-papers)
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+ Graph Adversarial Learning Literature, [[:octocat:Link]](https://github.com/safe-graph/graph-adversarial-learning-literature)
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+ A Complete List of All (arXiv) Adversarial Example Papers, [[🌐Link]](https://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html)
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