- pytorch implementation
- sample data for running
Please kindly cite our article if you use this repository
@inproceedings{DBLP:conf/recsys/ChenR18,
author = {Yifan Chen and
Maarten de Rijke},
title = {A Collective Variational Autoencoder for Top-N Recommendation with
Side Information},
booktitle = {Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems,
DLRS@RecSys 2018, Vancouver, BC, Canada, October 6, 2018},
pages = {3--9},
year = {2018},
crossref = {DBLP:conf/recsys/2018dlrs},
url = {https://doi.org/10.1145/3270323.3270326},
doi = {10.1145/3270323.3270326},
timestamp = {Wed, 21 Nov 2018 12:44:01 +0100},
biburl = {https://dblp.org/rec/bib/conf/recsys/ChenR18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
- python >= 3.6
- pytorch >= 1.0
- pytrec_eval: https://github.com/cvangysel/pytrec_eval
check specifications by
python cvae/cvae.py -h
pre-train with side information
python cvae/cvae.py --dir data --data music -a 10 -b 0.1 -m 50 -N 20 --layer 100 20 --save --gpu
refine by rating
python cvae/cvae.py --dir data --data music -a 1 -b 1 -m 30 -N 20 --layer 100 20 --load 1 --rating --gpu
If you want to test on CPU, simply remove --gpu.