Awesome Adversarial Learning on Recommender System (Updating)

👉 Table of Contents 👈

• Attack
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
• Defense
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
• Survey
• Resource
• Slides

Attack

2022

• PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion, WSDM, 📝Paper
• Targeted Data Poisoning Attack on News Recommendation System， Arxiv, 📝Paper
• FedRecAttack: Model Poisoning Attack to Federated Recommendation, ICDE, 📝Paper, :octocat:Code
• Poisoning Deep Learning based Recommender Model in Federated Learning Scenarios, IJCAI, 📝Paper

2021

• A Black-Box Attack Model for Visually-Aware Recommender Systems, WSDM, 📝Paper
• Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack, Information Sciences, 📝Paper
• Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data, KDD, 📝Paper
• Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems, KDD, 📝Paper
• Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction, RecSys, 📝Paper
• Membership Inference Attacks Against Recommender Systems, Arxiv, 📝Paper

2020

• Data Poisoning Attacks on Neighborhood-based Recommender Systems, ETT, 📝Paper
• Attacking Black-box Recommendations via Copying Cross-domain User Profiles, Arxiv, 📝Paper
• Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems, SIGIR, 📝Paper
• Adversarial Attacks on Linear Contextual Bandits, Arxiv, 📝Paper
• Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start, Arxiv, 📝Paper, :octocat:Code
• Influence Function based Data Poisoning Attacks to Top-N Recommender Systems, *https://arxiv.org/abs/2002.08025/abs/2002.08025)
• TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems, Dependable and Secure Machine Learning (DSML), 📝Paper, :octocat:Code
• Adversarial Attacks on Time Series, IEEE Transactions on Pattern Analysis and Machine Intelligence, 📝Paper
• Attacking Recommender Systems with Augmented User Profiles, Arxiv, 📝Paper
• Practical Data Poisoning Attack against Next-Item Recommendation, *https://dl.acm.org/doi/abs/10.1145/3366423.33799923366423.3379992)
• PoisonRec: An Adaptive Data Poisoning Framework for Attacking Black-box Recommender Systems, ICDE, 📝Paper
• Data Poisoning Attacks against Differentially Private Recommender Systems, SIGIR, 📝Paper
• Revisiting Adversarially Learned Injection Attacks Against Recommender Systems, RecSys, 📝Paper

2019

• Adversarial Attacks on an Oblivious Recommender, RecSys, 📝Paper
• Targeted Poisoning Attacks on Social Recommender Systems, IEEE Global Communications Conference (GLOBECOM), 📝Paper
• Data Poisoning Attacks on Graph Convolutional Matrix Completion，International Conference on Algorithms and Architectures for Parallel Processing, 📝Paper
• Data Poisoning Attacks on Stochastic Bandits, ICML, 📝Paper
• Data Poisoning Attacks on Cross-domain Recommendation, CIKM, 📝Paper
• Assessing the Impact of a User-Item Collaborative Attack on Class of Users, RecSys Workshop, 📝Paper

2018

• Poisoning attacks to graph-based recommender systems, Annual Computer Security Applications Conference (ACSAC), 📝Paper, :octocat:Code

2017

• Fake Co-visitation Injection Attacks to Recommender Systems, NDSS, 📝Paper
• Hybrid attacks on model-based social recommender systems, Physica A: Statistical Mechanics and its Applications, 📝Paper

2016

• Data Poisoning Attacks on Factorization-Based Collaborative Filtering, NIPS, 📝Paper, :octocat:Code
• Segment-Focused Shilling Attacks against Recommendation Algorithms in Binary Ratings-based Recommender Systems, International Journal of Hybrid Information Technology, 📝Paper
• Shilling attack models in recommender system, International Conference on Inventive Computation Technologies (ICICT), 📝Paper

Defense

2021

• Graph Embedding for Recommendation against Attribute Inference Attacks, *https://arxiv.org/pdf/2101.12549.pdff/2101.12549.pdf)
• Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality, Arxiv, 📝Paper

2020

• GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection, Arxiv, 📝Paper
• On Detecting Data Pollution Attacks On Recommender Systems Using Sequential GANs, ICML, 📝Paper
• A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering, Arxiv, 📝Paper
• Adversarial Collaborative Auto-encoder for Top-N Recommendation, Arxiv, 📝Paper
• Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems, Arxiv, 📝Paper
• Adversarial Learning to Compare: Self-Attentive Prospective Customer Recommendation in Location based Social Networks, WSDM, 📝Paper
• Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines, SIGIR, 📝Paper
• Directional Adversarial Training for Recommender Systems, ECAI, 📝Paper
• Shilling Attack Detection Scheme in Collaborative Filtering Recommendation System Based on Recurrent Neural Network, Future of Information and Communication Conference, 📝Paper
• Learning Product Rankings Robust to Fake Users， Arxiv, 📝Paper
• Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning, WSDM, 📝Paper
• Quick and accurate attack detection in recommender systems through user attributes, RecSys, 📝Paper
• Global and Local Differential Privacy for Collaborative Bandits, RecSys, 📝Paper
• Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World, RecSys, 📝Paper
• GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection, RecSys, 📝Paper

2019

• Adversarial Training Towards Robust Multimedia Recommender System, TKDE, 📝Paper, :octocat:Code
• Adversarial Collaborative Neural Network for Robust Recommendation, SIGIR, 📝Paper
• Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation, SIGIR, 📝Paper, :octocat:Code
• Adversarial tensor factorization for context-aware recommendation, RecSys, 📝Paper, [:octocat:Code]
• Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System, IEEE Access, 📝Paper
• Securing the Deep Fraud Detector in Large-Scale E-Commerce Platform via Adversarial Machine Learning Approach，*https://www.ntu.edu.sg/home/boan/papers/WWW19.pdfapers/WWW19.pdf)
• Shilling Attack Detection in Recommender System Using PCA and SVM, Emerging technologies in data mining and information security, 📝Paper

2018

• Adversarial Personalized Ranking for Recommendation, SIGIR, 📝Paper, :octocat:Code
• A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network, The Computer Journal, 📝Paper
• Adversarial Sampling and Training for Semi-Supervised Information Retrieval, *https://arxiv.org/abs/1506.05752/abs/1506.05752)
• Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks, IEEE Transactions on Multimedia, 📝Paper
• An Obfuscated Attack Detection Approach for Collaborative Recommender Systems, Journal of computing and information technology, 📝Paper

2017

• Detecting Abnormal Profiles in Collaborative Filtering Recommender Systems, Journal of Intelligent Information Systems, 📝Paper
• Detection of Profile Injection Attacks in Social Recommender Systems Using Outlier Analysis, IEEE Big Data, 📝Paper
• Prevention of shilling attack in recommender systems using discrete wavelet transform and support vector machine, Eighth International Conference on Advanced Computing (ICoAC), 📝Paper

2016

• Discovering shilling groups in a real e-commerce platform, Online Information Review, 📝Paper
• Shilling attack detection in collaborative filtering recommender system by PCA detection and perturbation, International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), 📝Paper
• Re-scale AdaBoost for attack detection in collaborative filtering recommender systems, KBS, 📝Paper
• SVM-TIA a shilling attack detection method based on SVM and target item analysis in recommender systems, Neurocomputing, 📝Paper

Survey

• A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks, ACM Computing Surveys (CSUR) 2021, 📝Paper
• Adversarial Machine Learning in Recommender Systems: State of the art and Challenges, Arxiv2020, 📝Paper
• A Survey of Adversarial Learning on Graphs, Arxiv2020, 📝Paper
• Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study, Arxiv2020, 📝Paper
• Shilling attacks against collaborative recommender systems: a review, Artificial Intelligence Review, 📝Paper
• Adversarial Attacks and Defenses in Images, Graphs and Text: A Review, Arxiv2019, 📝Paper
• A Survey of Attacks in Collaborative Recommender Systems, Journal of Computational and Theoretical Nanoscience 2019, 📝Paper
• Adversarial Attack and Defense on Graph Data: A Survey, Arxiv2018, 📝Paper
• Adversarial Machine Learning: The Case of Recommendation Systems, IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 📝Paper
• Recommender Systems: Attack Types and Strategies, AAAI2005, 📝Paper
• A Review of Attacks and Its Detection Attributes on Collaborative Recommender Systems, IJARCS2017, 📝Paper

Resource

• Awesome Graph Adversarial Learning :octocat:Link
• Awesome Graph Attack and Defense Papers :octocat:Link
• Graph Adversarial Learning Literature :octocat:Link
• A Complete List of All (arXiv) Adversarial Example Papers 🌐Link
• Robust Matrix Completion via Robust Gradient Descent 🌐Link
• **Adversarial Machine Learning in Recommender Systems:Literature Review and Future Visions ** :octocat:Link

Slides

• UCI Lecture 🌐Link
• RecSys2020 Tutorial :octocat:Link