Shilling Attacks against Recommender Systems

This repository contains our implementations for Shilling Attacks against Recommender Systems.

Folder structure:

• AUSH: The implementation of AUSH used in our CIKM'20 paper.
• Leg-UP: The implementation of Leg-UP and a unified framework for comparing Leg-UP with various attackers including AIA, DCGAN, WGAN, Random Attack, Average Attack, Segment Attack and Bandwagon Attack.
• Data: Recommendation datasets used in our experiments.

See README.md in each folder for more details.

Please kindly cite our papers if you find our implementations useful:

Chen Lin, Si Chen, Hui Li, Yanghua Xiao, Lianyun Li, and Qian Yang. 2020. Attacking Recommender Systems with Augmented User Profiles. In CIKM. 855–864.

@inproceedings{Lin2020Attacking,  
  author    = {Chen Lin and
               Si Chen and
               Hui Li and
               Yanghua Xiao and
               Lianyun Li and
               Qian Yang},
  title     = {Attacking Recommender Systems with Augmented User Profiles},
  booktitle = {{CIKM}},
  pages     = {855--864},
  year      = {2020}
}