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README.md
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README.md
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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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### 👉 Table of 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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- [Attack](#attack)
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- [2021](#2021)
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- [2020](#2020)
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- [2019](#2019)
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- [2018](#2018)
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- [2017](#2017)
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- [2016](#2016)
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- [Defense](#defense)
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- [2021](#2021-1)
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- [2020](#2020-1)
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- [2019](#2019-1)
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- [2018](#2018-1)
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- [2017](#2017-1)
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- [2016](#2016-1)
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- [Survey](#survey)
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- [Resource](#resource)
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- [Slides](#slides)
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<a class="toc" id ="1"></a>
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# Attack
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<a class="toc" id ="1-1"></a>
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## 2021
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+ **A Black-Box Attack Model for Visually-Aware Recommender Systems**, *WSDM*, [📝Paper](https://arxiv.org/abs/2011.02701)
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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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<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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<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), [:octocat: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), [:octocat:Code](https://github.com/fuying-wang/Data-poisoning-attacks-on-factorization-based-collaborative-filtering)
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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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<a class="toc" id ="2-1"></a>
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## 2021
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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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<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), [:octocat:Code](https://github.com/duxy-me/AMR)
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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), [:octocat:Code](https://github.com/hexiangnan/adversarial_personalized_ranking)
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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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+ **An Obfuscated Attack Detection Approach for Collaborative Recommender Systems**, *Journal of computing and information technology*, [📝Paper](https://hrcak.srce.hr/203982)
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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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+ **Re-scale AdaBoost for attack detection in collaborative filtering recommender systems**, *KBS*, [📝Paper](https://www.sciencedirect.com/science/article/pii/S0950705116000861)
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+ **SVM-TIA a shilling attack detection method based on SVM and target item analysis in recommender systems**, *Neurocomputing*, [📝Paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231216306038)
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<a class="toc" id ="3"></a>
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# Survey
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+ **Recommender Systems: Attack Types and Strategies**, *AAAI*2005, 📝[Paper](https://www.aaai.org/Papers/AAAI/2005/AAAI05-053.pdf)
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+ **A Review of Attacks and Its Detection Attributes on Collaborative Recommender Systems**, *IJARCS2017*, 📝[Paper](http://www.ijarcs.info/index.php/Ijarcs/article/download/4550/4100)
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<a class="toc" id ="4"></a>
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# Resource
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