VBAE: Variational Bandwidth Auto-encoder

The codes are associated with the following paper:

Collaborative Variational Bandwidth Auto-encoder for Recommender Systems,
Yaochen Zhu and Zhenzhong Chen.
IEEE Trans. Knowl. Data Eng., 2022. [pdf].

Environment

The codes are written in Python 3.6.5 with the following packages.

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

Datasets

The processed datasets can be found here.

For usage, create a data folder and move in the unzipped datasets.

Examples to run the codes

To reproduce the comparison results in Table 2:

Layerwise pretrain the user feature VAE:

python pretrain_vae.py --dataset Name --split [0-9]
Iteratively train the collarabotive and feature part of VBAE:

python train_vbae.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

To reproduce the bandwidth analysis results in Table 3:

Summarize the average, std and corr of bandwidth into the model folder

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

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

Citation

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

@article{vbae_zhu2021,
  title={Variational Bandwidth Auto-encoder for Hybrid Recommender Systems},
  author={Zhu, Yaochen and Chen, Zhenzhong},
  booktitle={IEEE Trans. Knowl. Data Eng.},
  year={2022},
}