shelveyj / coserec

Contrastive Self-supervised Sequential Recommendation with Robust Augmentation

Introduction

Contrastive Self-supervised Sequential Recommendation with Robust Augmentation (CoSeRec)

Source code for paper: Contrastive Self-supervised Sequential Recommendation with Robust Augmentation

Model architecture:

Data Augmentations:

Reference

Please cite our paper if you use this code.

@article{liu2021contrastive,
  title={Contrastive self-supervised sequential recommendation with robust augmentation},
  author={Liu, Zhiwei and Chen, Yongjun and Li, Jia and Yu, Philip S and McAuley, Julian and Xiong, Caiming},
  journal={arXiv preprint arXiv:2108.06479},
  year={2021}
}

Implementation

Requirements

Python >= 3.7
Pytorch >= 1.2.0
tqdm == 4.26.0

Datasets

Four prepared datasets are included in data folder.

Train Model

To train CoSeRec on Sports_and_Outdoors dataset, change to the src folder and run following command:

bash sports.sh

You can train CoSeRec on Beauty or Yelp in a similar way.

The script will automatically train CoSeRec and save the best model found in validation set, and then evaluate on test set. You are expected to get following results after training:

'HIT@5': '0.0287', 'NDCG@5': '0.0194', 'HIT@10': '0.0437', 'NDCG@10': '0.0242', 'HIT@20': '0.0635', 'NDCG@20': '0.0292'

Evaluate Model

You can directly evaluate a trained model on test set by running:

python main.py --data_name Sports_and_Outdoors --model_idx 0 --do_eval

We provide a model that trained on Sports_and_Games, Beauty, and Yelp in ./src/output folder. Please feel free to test is out.

Acknowledgement

• Transformer and training pipeline are implemented based on S3-Rec. Thanks them for providing efficient implementation.