135 lines
9.1 KiB
Markdown
135 lines
9.1 KiB
Markdown
<a class="toc" id="table-of-contents"></a>
|
|
|
|
# Awesome Adversarial Learning on Recommender System (Updating)
|
|
[](https://github.com/sindresorhus/awesome)
|
|
[](http://makeapullrequest.com)
|
|
|
|
### 👉 Table of Contents 👈
|
|
+ [Attack](#1)
|
|
+ [2020](#1-1)
|
|
+ [2019](#1-2)
|
|
+ [2018](#1-3)
|
|
+ [2017](#1-4)
|
|
+ [2016](#1-5)
|
|
+ [Defense](#2)
|
|
+ [2020](#2-1)
|
|
+ [2019](#2-2)
|
|
+ [2018](#2-3)
|
|
+ [2017](#2-4)
|
|
+ [2016](#2-5)
|
|
+ [Survey](#3)
|
|
+ [Resource](#4)
|
|
|
|
|
|
<a class="toc" id ="1"></a>
|
|
|
|
# Attack
|
|
|
|
<a class="toc" id ="1-1"></a>
|
|
|
|
## 2020
|
|
+ **Data Poisoning Attacks on Neighborhood-based Recommender Systems**, *ETT*, [📝Paper](https://arxiv.org/abs/1912.04109)
|
|
+ **Attacking Black-box Recommendations via Copying Cross-domain User Profiles**, *Arxiv*, [📝Paper](https://arxiv.org/abs/2005.08147)
|
|
+ **Attacking Black-box Recommendations via Copying Cross-domain User Profiles**, *Arxiv*, [📝Paper](https://arxiv.org/abs/2005.08147)
|
|
+ **Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems**, *Arxiv*, [📝Paper](https://arxiv.org/abs/2006.07934)
|
|
+ **Adversarial Attacks on Linear Contextual Bandits**, *Arxiv*, [📝Paper](https://arxiv.org/pdf/2002.03839)
|
|
+ **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), [:octocat:Code](https://github.com/liuzrcc/AIP)
|
|
+ **Influence Function based Data Poisoning Attacks to Top-N Recommender Systems**, *WWW*, [📝Paper](https://arxiv.org/abs/2002.08025)
|
|
+ **TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems**, *Dependable and Secure Machine Learning (DSML)*, [📝Paper](http://sisinflab.poliba.it/publications/2020/DMM20/PID6442119.pdf), [:octocat:Code](https://github.com/sisinflab/TAaMR)
|
|
|
|
|
|
|
|
<a class="toc" id ="1-2"></a>
|
|
|
|
## 2019
|
|
+ **Adversarial Attacks on an Oblivious Recommender**, *RecSys*, [📝Paper](https://dl.acm.org/doi/10.1145/3298689.3347031)
|
|
+ **Targeted Poisoning Attacks on Social Recommender Systems**, *IEEE Global Communications Conference (GLOBECOM)*, [📝Paper](https://ieeexplore.ieee.org/document/9013539)
|
|
|
|
|
|
|
|
<a class="toc" id ="1-3"></a>
|
|
|
|
## 2018
|
|
+ **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)
|
|
|
|
|
|
<a class="toc" id ="1-4"></a>
|
|
|
|
## 2017
|
|
+ **Fake Co-visitation Injection Attacks to Recommender Systems**, *NDSS*, [📝Paper](http://people.duke.edu/~zg70/papers/ndss17-attackRS.pdf)
|
|
+ **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)
|
|
<a class="toc" id ="1-5"></a>
|
|
|
|
## 2016
|
|
+ **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)
|
|
+ **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)
|
|
+ **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)
|
|
+ **Shilling attack models in recommender system**, *International Conference on Inventive Computation Technologies (ICICT)*, [📝Paper](https://ieeexplore.ieee.org/document/7824865)
|
|
|
|
|
|
<a class="toc" id ="2"></a>
|
|
|
|
# Defense
|
|
|
|
|
|
<a class="toc" id ="2-1"></a>
|
|
|
|
## 2020
|
|
+ **A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering**, *Arxiv*, [📝Paper](https://arxiv.org/abs/2004.14734)
|
|
+ **Adversarial Collaborative Auto-encoder for Top-N Recommendation**, *Arxiv*, [📝Paper](https://arxiv.org/abs/1808.05361)
|
|
+ **Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems**, *Arxiv*, [📝Paper](https://arxiv.org/abs/2006.07934)
|
|
+ **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)
|
|
+ **Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines**, *SIGIR*, [📝Paper](http://jiyang3.web.engr.illinois.edu/files/fm-rt.pdf)
|
|
+ **Directional Adversarial Training for Recommender Systems**, *ECAI*, [📝Paper](http://ecai2020.eu/papers/300_paper.pdf)
|
|
|
|
<a class="toc" id ="2-2"></a>
|
|
|
|
## 2019
|
|
+ **Adversarial Training Towards Robust Multimedia Recommender System**, *TKDE*, [📝Paper](https://graphreason.github.io/papers/35.pdf), [:octocat:Code](https://github.com/duxy-me/AMR)
|
|
+ **Adversarial Collaborative Neural Network for Robust Recommendation**, *SIGIR*, [📝Paper](https://www.researchgate.net/publication/332861957_Adversarial_Collaborative_Neural_Network_for_Robust_Recommendation)
|
|
+ **Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation**, *SIGIR*, [📝Paper](http://web.cs.wpi.edu/~kmlee/pubs/tran19sigir.pdf), [:octocat:Code](https://github.com/thanhdtran/MASR)
|
|
+ **Adversarial tensor factorization for context-aware recommendation**, *RecSys*, [📝Paper](https://dl.acm.org/doi/10.1145/3298689.3346987), [:octocat:Code]
|
|
+ **Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System**, *IEEE Access*, [📝Paper](https://ieeexplore.ieee.org/document/8946325)
|
|
|
|
|
|
|
|
<a class="toc" id ="2-3"></a>
|
|
|
|
## 2018
|
|
+ **Adversarial Personalized Ranking for Recommendation**, *SIGIR*, [📝Paper](https://dl.acm.org/citation.cfm?id=3209981), [:octocat:Code](https://github.com/hexiangnan/adversarial_personalized_ranking)
|
|
+ **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)
|
|
+ **Adversarial Sampling and Training for Semi-Supervised Information Retrieval**, *WWW*, [📝Paper](https://arxiv.org/abs/1506.05752)
|
|
+ **Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks**, *IEEE Transactions on Multimedia*, [📝Paper](https://ieeexplore.ieee.org/document/8576563)
|
|
+ **Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning**, *WSDM*, [📝Paper](https://arxiv.org/abs/1911.09872)
|
|
|
|
<a class="toc" id ="2-4"></a>
|
|
|
|
## 2017
|
|
+ **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)
|
|
+ **Detection of Profile Injection Attacks in Social Recommender Systems Using Outlier Analysis**, *IEEE Big Data*, [📝Paper](http://www.cs.ucf.edu/~anahita/08258235.pdf)
|
|
+ **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)
|
|
<a class="toc" id ="2-5"></a>
|
|
|
|
## 2016
|
|
+ **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)
|
|
|
|
<a class="toc" id ="3"></a>
|
|
|
|
# Survey
|
|
|
|
+ **Adversarial Machine Learning in Recommender Systems: State of the art and Challenges**, *Arxiv2020*, [📝Paper](https://arxiv.org/abs/2005.10322)
|
|
+ **A Survey of Adversarial Learning on Graphs**, *Arxiv2020*, [📝Paper](https://arxiv.org/abs/2003.05730)
|
|
+ **Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study**, *Arxiv2020*, [📝Paper](https://arxiv.org/abs/2003.00653)
|
|
+ **Adversarial Attacks and Defenses in Images, Graphs and Text: A Review**, *Arxiv2019*, [📝Paper](https://arxiv.org/abs/1909.08072)
|
|
+ **Adversarial Attack and Defense on Graph Data: A Survey**, *Arxiv2018*, [📝Paper](https://arxiv.org/abs/1812.10528)
|
|
|
|
|
|
<a class="toc" id ="4"></a>
|
|
# Resource
|
|
|
|
+ Awesome Graph Adversarial Learning, [:octocat:Link](https://github.com/gitgiter/Graph-Adversarial-Learning)
|
|
+ Awesome Graph Attack and Defense Papers, [:octocat:Link](https://github.com/ChandlerBang/awesome-graph-attack-papers)
|
|
+ Graph Adversarial Learning Literature, [:octocat:Link](https://github.com/safe-graph/graph-adversarial-learning-literature)
|
|
+ A Complete List of All (arXiv) Adversarial Example Papers, [🌐Link](https://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html)
|
|
|