zeng1028796085/cosrec

Code for CosRec: 2D Convolutional Neural Networks for Sequential Recommendation (CIKM-19)

CosRec: 2D Convolutional Neural Networks for Sequential Recommendation

This is our PyTorch implementation for the paper:

CosRec: 2D Convolutional Neural Networks for Sequential Recommendation, CIKM-2019

[arXiv] [GitHub]

The code is tested on a Linux server (w/ NVIDIA GeForce Titan X Pascal) with PyTorch 1.1.0 and Python 3.7.

Requirements

• Python 3
• PyTorch v1.0+ (v0.4+ might also work)

Training

To train our model on ml1m (with default hyper-parameters):

python train.py --dataset=ml1m

or on gowalla (change a few hyper-paras based on dataset statistics):

python train.py --dataset=gowalla --d=100 --fc_dim=50 --l2=1e-6

You should be able to obtain MAPs of ~0.188 and ~0.098 on ML-1M and Gowalla respectively, with the above settings.

Datasets

• Datasets are organized into 2 separate files: train.txt and test.txt

• Same as other data format for recommendation, each file contains a collection of triplets:

  user item rating

  The only difference is the triplets are organized in time order.

• As the problem is Sequential Recommendation, the rating doesn't matter, so we convert them all to 1.

Citation

If you find this repository useful, please cite our paper:

@inproceedings{yan2019cosrec,
  title={CosRec: 2D Convolutional Neural Networks for Sequential Recommendation},
  author={Yan, An and Cheng, Shuo and Kang, Wang-Cheng and Wan, Mengting and McAuley, Julian},
  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
  pages={2173--2176},
  year={2019},
  organization={ACM}
}

Acknowledgments

This project is built on top of Spotlight and Caser. Thanks Maciej and Jiaxi for their contributions to the community.