ARLib

An open-source framework for conducting data poisoning attacks on recommendation systems, designed to assist researchers and practitioners. This repo is released with our survey paper on poisoning attack against recommender system.

Members:
Zongwei Wang, Chongqing University, China, zongwei@cqu.edu.cn
Hao Ma, Chongqing University, China, ma_hao@cqu.edu.cn
Chenyu Li, Chongqing University, chenyu_li@stu.cqu.edu.cn

Supported by:
Prof. Min Gao, Chongqing University, China, gaomin@cqu.edu.cn
ARC Training Centre for Information Resilience (CIRES), University of Queensland, Australia

Framework

Usage

Two configure files attack_parser.py and recommend_parser.py are in the directory named conf, and you can select and configure the recommendation model and attack model by modifying the configuration files.
Run main.py.

Implemented Models

Recommend Model	Paper
GMF	Yehuda et al. Matrix Factorization Techniques for Recommender Systems, IEEE Computer'09.
WRMF	Hu et al.Collaborative Filtering for Implicit Feedback Datasets, KDD'09.
NCF	He et al. Neural Collaborative Filtering, WWW'17.
NGCF	Wang et al. Neural Graph Collaborative Filtering, SIGIR'19.
LightGCN	He et al. Lightgcn: Simplifying and powering graph convolution network for recommendation, SIGIR'2020.
SSL4Rec	Yao et al. Self-supervised learning for large-scale item recommendations. CIKM'2021.
NCL	Lin et al. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. WWW'2022.
SGL	Wu et al. Self-supervised Graph Learning for Recommendation, SIGIR'21.
SimGCL	Yu et al. Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation, SIGIR'22.
XSimGCL	Yu et al. XSimGCL: Towards extremely simple graph contrastive learning for recommendation, TKDE'23.

Attack Model	Paper	Case
NoneAttack	N/A	Black
RandomAttack	Lam et al. Shilling Recommender Systems for Fun and Profit. WWW'2004	Black
BandwagonAttack	Gunes et al. Shilling Attacks against Recommender Systems: A Comprehensive Survey. Artif.Intell.Rev.'2014	Black
AUSH	Lin C et al. Attacking recommender systems with augmented user profiles. CIKM'2020.	Gray
LegUP	Lin C et al. Shilling Black-Box Recommender Systems by Learning to Generate Fake User Profiles. IEEE Transactions on Neural Networks and Learning Systems'2022.	Gray
GOAT	Wu et al. Ready for emerging threats to recommender systems? A graph convolution-based generative shilling attack. Information Sciences'2021.	Gray
FedRecAttack	Rong et al. Fedrecattack: Model poisoning attack to federated recommendation. ICDE'2022.	Gray
A_ra	Rong et al. Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios. IJCAI'2022.	Gray
PGA	Li et al. Data poisoning attacks on factorization-based collaborative filtering. NIPS'2016.	White
DL_Attack	Huang et al. Data poisoning attacks to deep learning based recommender systems. arXiv'2021	White
PipAttack	Zhang et al. Pipattack: Poisoning federated recommender systems for manipulating item promotion. WSDM'2022.	Gray
RAPU	Zhang et al. Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data. KDD'2021.	White
PoisonRec	Song et al. Poisonrec: an adaptive data poisoning framework for attacking black-box recommender systems. ICDE'2021.	Black
CLeaR	Wang et al. Poisoning Attacks Against Contrastive Recommender Systems. arXiv'2023	White
GTA	Wang et al. Revisiting data poisoning attacks on deep learning based recommender systems. ISCC 2023	Black

Implement Your Model

Determine whether you want to implement the attack model or the recommendation model, and then add the file under the corresponding directory.

If you have an attack method, make sure：

Whether you need information of the recommender model, and then set self.recommenderGradientRequired=True.
Whether you need information of training recommender model, and then set self.recommenderModelRequired=True.
Reimplement function posionDataAttack()

If you have a recommender method, reimplement the following functions：

init()
posionDataAttack()
save()
predict()
evaluate()
test()

Downlod Dataset

BAIDU DISK
Link: https://pan.baidu.com/s/1Gw0SI_GZsykPQEngiMvZgA?pwd=akgm
key: akgm

Google Drive
Link: https://drive.google.com/drive/folders/1QLDickAMEuhi8mUOyAa66dicCTd40CG5?usp=sharing

Requirements

base==1.0.4
numba==0.53.1
numpy==1.18.0
scipy==1.4.1
torch==1.7.1

Reference

If you find this repo helpful to your research, please cite our paper.

@article{wang2024poisoning,
  title={Poisoning Attacks against Recommender Systems: A Survey},
  author={Wang, Zongwei and Gao, Min and Yu, Junliang and Ma, Hao and Yin, Hongzhi and Sadiq, Shazia},
  journal={arXiv preprint arXiv:2401.01527},
  year={2024}
}

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