MD-CVAE: Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems

The codes are associated with the following paper:

Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems,
Yaochen Zhu and Zhenzhong Chen,
The Web Conference (WWW) 2022. [paper] [slides]

Environment

The codes are written in Python 3.6.5.

• numpy == 1.16.3
• pandas == 0.21.0
• tensorflow-gpu == 1.15.0
• tensorflow-probability == 0.8.0

Datasets

The raw features of the established movielen-sub dataset can be obtained here: [Google Drive], [Baidu] (PIN:r8f0)

The processed datasets can be found here: [Google Drive], [Baidu] (PIN:l8wr)

For usage, please unzip the processed datasets and copy them into the data folder.

Examples to run the codes

1. To reproduce the in-matrix prediction results:

• Pretrain the dual item content embedding VAE:

  python pretrain_vae.py --dataset Name --split [0-9]

• Iteratively train the collaborative and content VAEs:

  python train_vae.py --dataset Name --split [0-9]

• Evaluate the model and summarize the results into a pivot table

  python predict.py --dataset Name --split [0-9]

  python summarize.py

2. To reproduce the out-of-matrix prediction results:

• First, please change to the cold_start folder.

• Download the processed data [Google Drive], [Baidu] (PIN:f4f7)

• The way to run the codes and summarize the results is similar to the in-matrix case.

For advanced argument usage, run the code with --help argument.

Reference

if you find the codes helpful, please kindly cite our paper. Thanks!

@inproceedings{MDCVAE-WWW2022,
  title={Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems},
  author={Zhu, Yaochen and Chen, Zhenzhong},
  booktitle={Proceedings of the ACM Web Conference 2022},
  year={2022},
}