AIP: Adversarial Item Promotion

This repository releases the PyTorch implementation of "AIP attacks" in our paper "Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start".

Figure below illustrates the mechanics of AIP attack.

How to use the code:

0. Prerequisites

We carry out experiments on Ubuntu 18.04 environemnt with 32 cpu cores and 32G memory. A GPU is essential for efficiency.

Python 3
PyTorch 1.4.0

1. Prepare datasets

Clone the AIP repository and download data.

git clone https://github.com/liuzrcc/AIP.git
cd AIP/data
bash download_data.sh

Please also download visual features and list of cold item items at this link.

2. Train first recommender BPR and second stage visual ranler DVBPR and VBPR

cd ./train/
python BPRtrain.py -data_set=amazon -gpu_id=0 -factor_num=64 -epoch=2000 -batch_size=4096 -lambda1=1e-3 -learning_rate=0.01 -num_workers=6
python DVBPRtrain.py -data_set=amazon -gpu_id=0 -factor_num=100 -epoch=20 -batch_size=128 -lambda1=1e-3 -lambda1=1 -learning_rate=1e-4 -num_workers=6
python VBPRtrain.py -data_set=amazon -gpu_id=0 -factor_num=100 -epoch=2000 -batch_size=512 -lambda1=1e-4 -learning_rate=1e-4 -num_workers=4
python AMR.py -data_set=amazon -gpu_id=0 -factor_num=100 -epoch=2000 -batch_size=512 -lambda1=1e-4 -learning_rate=1e-4 -num_workers=4

Pre-trained models are available at this link.

3. Generate first stage candidate set and calculate visual ranker score for candidate set

python gen_candidate_set.py -task=BPR-DVBPR -data_set=amazon -gpu_id=0 -model_path=./models/ -score_path=./bpr_score_index/
python gen_candidate_set.py -task=VBPR -data_set=amazon -gpu_id=0 -model_path=./models/ -score_path=./bpr_score_index/
python gen_candidate_set.py -task=AlexRank -data_set=amazon -gpu_id=0 -model_path=./models/ -score_path=./bpr_score_index/
python gen_candidate_set.py -task=AMR -data_set=amazon -gpu_id=0 -model_path=./models/ -score_path=./bpr_score_index/

Pre-calculated index and score are available at this link.

4. Mount AIP attacks

Choose model (DVBPR, VBPR, AlexRank) to attack, and also choose attack methods (INSA or EXPA).

python mount_AIP.py -data_set=amazon -gpu_id=0 -model_to_attack=DVBPR -attack_type=INSA

Adversarial item images are save in ./adv_output/.

5. Evaluation of AIP attacks

python eval.py -model_to_eval=DVBPR -data_set=amazon -gpu_id=0 -adv_item_path=amazon_INSA

Evaluation results are saved in ./results/.

AIP Examples

Examples generated by INSA, EXPA and SEMA.

Visualization of attacks:

A t-SNE 2-D visualization of cooperative item and adversarial items are shown below:

Please cite the following paper if you use AIP in your research.

  @inproceedings{liu2020adversarial,
  Author = {Zhuoran Liu and Martha Larson},
  Title = {Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start},
  Year = {2021},
  booktitle = {Proceedings of The Web Conference (WWW)},
  publisher = {ACM}      
  }

The copyright of all the images belongs to the image owners.