caipeiming / CML

The implementation of Contrastive Meta Learning with Behavior Multiplicity forRecommendation

CML

PyTorch implementation for paper: Contrastive Meta Learning with Behavior Multiplicity forRecommendationPaper in arXiv.

Dependencies

• Python 3.6
• torch==1.8.1+cu111
• scipy==1.6.2
• tqdm==4.61.2

Dataset Preparation

Raw data：

• IJCAI contest: https://tianchi.aliyun.com/dataset/dataDetail?dataId=47
• Retail Rocket: https://www.kaggle.com/retailrocket/ecommerce-dataset
• Tmall: https://tianchi.aliyun.com/dataset/dataDetail?dataId=649

Usage

You need to create the History/ and the Models/ directories.

• mkdir /History
• mkdir /Model

The command to train CML on the Tmall/IJCAI/Retailrocket datasets are as follows. The commands specify the hyperparameter settings that generate the reported results in the paper.

• Tmall

python .\main.py --dataset=Tmall --opt_base_lr=1e-3 --opt_max_lr=5e-3 --opt_weight_decay=1e-4 --meta_opt_base_lr=1e-4 --meta_opt_max_lr=2e-3 --meta_opt_weight_decay=1e-4 --meta_lr=1e-3 --batch=8192 --meta_batch=128 --SSL_batch=18

• IJCAI

python .\main.py --dataset=IJCAI_15 --sampNum=10 --opt_base_lr=1e-3 --opt_max_lr=2e-3 --opt_weight_decay=1e-4 --meta_opt_base_lr=1e-4 --meta_opt_max_lr=1e-3 --meta_opt_weight_decay=1e-4 --meta_lr=1e-3 --batch=8192 --meta_batch=128 --SSL_batch=30 

• Retailrocket

python .\main.py --dataset='retailrocket' --sampNum=40 --lr=3e-4 --opt_base_lr=1e-4 --opt_max_lr=1e-3 --opt_weight_decay=1e-4 --opt_weight_decay=1e-4 --meta_opt_base_lr=1e-4 --meta_opt_max_lr=1e-3 --meta_opt_weight_decay=1e-3 --meta_lr=1e-3 --batch=2048 --meta_batch=128 --SSL_batch=15

Citation

If you want to use our codes and datasets in your research, please cite:

@inproceedings{wei2022contrastive,
  title={Contrastive meta learning with behavior multiplicity for recommendation},
  author={Wei, Wei and Huang, Chao and Xia, Lianghao and Xu, Yong and Zhao, Jiashu and Yin, Dawei},
  booktitle={Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
  pages={1120--1128},
  year={2022}
}