This is the pytorch implementation of our paper at SIGIR 2021:

Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue

Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua.

• Anaconda 3
• python 3.7.3
• pytorch 1.4.0
• numpy 1.16.4

• model_name: MMGCN.
• l_r: learning rate. Default: 1e-3.
• weight_decay: the hyper-parameter for weight decay. Default: 1e-3.
• gpu_id: the gpu used for training.

Other parameter settings can be found in train.py. We keep the default setings as MMGCN.

python train.py --model_name=$1 --l_r=$2 --weight_decay=$3

or use run.sh

sh run.sh gpu_id model_name l_r weight_decay

The log file will be in the ./log/ folder.

1. Download the checkpoints released by us from Google drive.
2. Put the '.pth' file into the model_1 folder.
3. Run inference.py or run_inference.sh:

python inference.py --model_name=$2 --l_r=$3 --weight_decay=$4 --log_name="$2_tiktok_$3lr_$4wd_$5"

sh run_inference.sh gpu_id model_name l_r weight_decay log_name

1. Train MMGCN on Tiktok:

cd ./code/tiktok
CUDA_VISIBLE_DEVICES=0 python main.py --model_name=MMGCN --l_r=1e-3 --weight_decay=1e-3

2. Inference MMGCN on Adressa

cd ./code/adressa
sh run_inference.sh 0 MMGCN 1e-3 1e-3 TIE

If you use our code, please kindly cite:

@inproceedings{wang2021Clicks,
  title={Clicks can be Cheating: Counterfactual Recommendationfor Mitigating Clickbait Issue},
  author={Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua},
  booktitle={SIGIR},
  year={2021},
  publisher={ACM}
}

Thanks to the MMGCN implementation:

• MMGCN from Yinwei Wei.

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