This project is for the paper: Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training, in proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021: 1074-1083.
The code was developed on Python 3.6 and tensorflow 1.15.0.
mkdir poison_data
Put the poisoning data generated by attacks in this folder, and the poisoning data is named dataset_attack_type_attack_size.npy, e.g., ml-100k_random_30.npy.
usage: python main.py [--data DATA_NAME] [--gpu GPU_ID]
[--top_k TOP_K] [--extend ERM_USERS] [--target_index TARGET_ITEMS]
optional arguments:
--dataset DATA_NAME
Supported: filmtrust, ml-100k, ml-1m, yelp.
--gpu GPU_ID
GPU ID, default is 0.
--top_k TOP_K
HR@top_k, default is 50.
--extend ERM_USERS
The number of ERM users, default is 50.
--target_index TARGET_ITEMS
The index of predefined target item list: 0, 1 for ml-100k, 2,3 for ml-1m, 4,5 for filmtrust, 6,7 for yelp.
python main.py --dataset ml-100k --gpu 0 --top_k 50 --extend 50 --target_index 0
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