MSAP: Multi-Step Adversarial Perturbations on Recommender Systems Embeddings

GitHub repository of the FLAIRS2021 paper: MSAP: Multi-Step Adversarial Perturbations on Recommender Systems Embeddings, published by Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra.

Paper available at Sisinflab publications web page.

Table of Contents:

• Requirements
• Reproduce paper results

Requirements

To begin with, please make sure your system has these installed:

• Python 3.6.8
• CUDA 10.1
• cuDNN 7.6.4

Then, install all required Python dependencies with the command:

pip install -r requirements.txt

Reproduce paper results

Here we describe the steps to reproduce the results presented in the paper.

1. Train BPR-MF recommender model

First of all, train the BPR-MF recommender model by running:

python train.py \
  --dataset <dataset_name> \
  --rec bprmf \
  --epochs 2000 \
  --k 10

2. Train AMF recommender model

The, train AMF model by running:

python train.py \
  --dataset <dataset_name> \
  --rec apr \
  --epochs 2000 \
  --restore_epochs 100 \
  --k 10 \
  --adv_type fgsm\
  --adv_eps 0.5\
  --adv_reg 1

adv_type, adv_eps, adv_reg are parameters set to specify the type of fgsm-like attack used to apply the adversarial regularization.

3. Run Attacks

Based upon the produced recommender model we can run the attacks:

python run_attack.py \
  --dataset <dataset_name> \
  --rec <recommendr_name> \
  --attack_type <attack_type> \
  --attack_eps <attack_eps> \
  --attack_step_size <attack_step_size> \
  --attack_iteration <attack_iteration> \
  --best 1

where attack_type can be [fgsm, bim, pgd], attack_eps is the budget perturbation (epsilon), attack_step_size is the step size (e.g., 4) used in the iterative attacks, attack_iteration is the number of iterations.

4. Recommendation Evaluation

The attack results are store as recommendation lists under the directory ./rec_results/<dataset_name>/<model_name>/file_name.tsv. Each result file can be evaluated with different evaluation frameworks.

Authors Team

• Vito Walter Anelli ([email protected])
• Alejandro Bellogín ([email protected])
• Yashar Deldjoo ([email protected])
• Tommaso Di Noia ([email protected])
• Felice Antonio Merra^* ([email protected])

^* Corresponding author