Temporally and Distributionally Robust Optimization for Cold-start Recommendation

💡 This is the pytorch implementation of our paper

Temporally and Distributionally Robust Optimization for Cold-start Recommendation

Xinyu Lin, Wenjie Wang, Jujia Zhao, Yongqi Li, Fuli Feng, Tat-Seng Chua

Environment

• Anaconda 3
• python 3.7.11
• pytorch 1.10.0
• numpy 1.21.4
• kmeans_pytorch

Usage

Data

The experimental data are in './data' folder, including Amazon, Micro-video, and Kwai.

🔴 Training

python main.py --model_name=$1 --data_path=$2 --batch_size=$3 --l_r=$4 --reg_weight=$5 --num_group=$6 --num_period=$7 --mu=$8 --eta=$9 --lam=$10 --split_mode=$11 --log_name=$12 --gpu=$13

or use run.sh

sh run.sh <model_name> <dataset> <batch_size> <lr> <reg_weight> <num_group> <num_period> <mu> <eta> <lam> <split_mode> <logname> <gpu_id>

• The log file will be in the './code/log/' folder.
• The explanation of hyper-parameters can be found in './code/main.py'.
• The default hyper-parameter settings are detailed in './code/hyper-parameters.txt'.

🌟 TDRO is a model-agnostic training framework and can be applied to any cold-start recommender model. You can simply create your cold-start recommender model script in './code' folder, in a similar way to "model_CLCRec.py". Alternatively, you may adopt the function train_TDRO in "Train.py" to your own code for training your cold-start recommender model via TDRO.

🔵 Inference

Get the results of TDRO by running inference.py:

python inference.py --inference --data_path=$1 --ckpt=$2 --gpu=$3

or use inference.sh

sh inference.sh dataset <ckpt_path> <gpu_id>

⚪ Examples

1. Train on Amazon dataset

cd ./code
sh run.sh TDRO amazon 1000 0.001 0.001 5 5 0.2 0.2 0.3 global log 0

2. Inference

cd ./code
sh inference.sh amazon <ckpt_path> 0

Citation

If you find our work is useful for your research, please consider citing:

@inproceedings{lin2023temporally,
      title={Temporally and Distributionally Robust Optimization for Cold-start Recommendation}, 
      author={Xinyu Lin, Wenjie Wang, Jujia Zhao, Yongqi Li, Fuli Feng, and Tat-Seng Chua},
      booktitle={AAAI},
      year={2024}
}

License

NUS © NExT++