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BiVAE: Bilateral Variational Autoencoder
This is the code for the paper:
Bilateral Variational Autoencoder for Collaborative Filtering <br> Quoc-Tuan Truong, Aghiles Salah, and Hady W. Lauw <br> Presented at WSDM 2021
<img align="center" width="100%" src="gm.png">If you find the code and data useful in your research, please cite:
@inproceedings{truong2021bilateral,
title={Bilateral Variational Autoencoder for Collaborative Filtering},
author={Truong, Quoc-Tuan, and Salah, Aghiles, and Lauw, Hady W},
booktitle={ACM International Conference on Web Search and Data Mining, {WSDM} 2021}
year={2021},
}
How to run
pip install -r requirements.txt
Run BiVAE model:
python bivae.py -d office -k 20 -e '[40]' -a tanh -l pois -ne 500 -bs 128 -lr 0.001 -tk 50 -v
Run BiVAE model with Constrained Adaptive Priors (CAP):
- CAP requires feature learning, here we use vanilla VAE as an example:
# user side
python feature_learning.py -d office -w user -k 20 -e '[100]' -a tanh -l pois -ne 100 -bs 128 -lr 0.001 -s 123 -v
# item side
python feature_learning.py -d office -w item -k 20 -e '[100]' -a tanh -l pois -ne 100 -bs 128 -lr 0.001 -s 123 -v
- When user/item features are ready, we can train BiVAE with CAP:
python bivae_cap.py -d office -uc -ic -k 20 -e '[40]' -a tanh -l pois -ne 500 -bs 128 -lr 0.001 -tk 50 -v
Related
Contact
Questions and discussion are welcome: www.qttruong.com