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DATASET.md

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DATASET

Download the original dataset from ml-1m and Amazon. Then follow the steps below to process the dataset.

Process:

  1. Divide the original dataset into three non-overlapping parts: shadow_dataset, target dataset and dataset for vectorization, which are used for training shadow recommender system, target recommender system and obtaining the item representation respectively.
cd DL-MIA/DL-MIA-SR/Dataset/
python process_amazon_Beauty.py
python process_ml1m.py
  1. Get the latent representation of items by LFM(Latent factor model).
cd RecSys-master/
python mainCF.py
  1. Train shadow model and target model using shadow dataset and target dataset respectively. Then generate recommendaiton lists for member and nonmember(Based on popularity). Note that for different recommendation models, further dataset processing and file path changes are required here. We provide the corresponding code, which is not described in detail here.
# e.g. BERT4Rec
cd ../Recommender/BERT4Rec-Pytorch-master/
sh BERT4Rec.sh
  1. Generate difference feature vector.
# e.g. BERT4Rec
cd ../../Biased-Attack/SMDD/AttackModel/
sh attack.sh
  1. Process data to match model inputs.
# e.g. BERT4Rec
cd ../../../Joint-Training/
sh dataPreProcess.sh