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Graph Convolutional Matrix Completion

Tensorflow based implemention of Graph Convolutional Matrix Completion for recommender systems, based on our paper:

Rianne van den Berg, Thomas N. Kipf, Max Welling, Graph Convolutional Matrix Completion (2017)

Installation

python setup.py install

Requirements

Usage

To reproduce the experiments mentioned in the paper you can run the following commands:

Douban

python train.py -d douban --accum stack -do 0.7 -nleft -nb 2 -e 200 --features --feat_hidden 64 --testing

Flixster

python train.py -d flixster --accum stack -do 0.7 -nleft -nb 2 -e 200 --features --feat_hidden 64 --testing

Yahoo Music

python train.py -d yahoo_music --accum stack -do 0.7 -nleft -nb 2 -e 200 --features --feat_hidden 64 --testing

Movielens 100K on official split with features

python train.py -d ml_100k --accum stack -do 0.7 -nleft -nb 2 -e 1000 --features --feat_hidden 10 --testing

Movielens 100K on official split without features

python train.py -d ml_100k --accum stack -do 0.7 -nleft -nb 2 -e 1000 --testing

Movielens 1M

python train.py -d ml_1m --data_seed 1234 --accum sum -do 0.7 -nsym -nb 2 -e 3500 --testing

Movielens 10M

python train_mini_batch.py -d ml_10m --data_seed 1234 --accum stack -do 0.3 -nsym -nb 4 -e 20 --testing

Note: 10M dataset training does not fit on GPU memory (12 Gb), therefore this script uses a naive version of mini-batching. Script can take up to 24h to finish.

Cite

Please cite our paper if you use this code in your own work:

@article{vdberg2017graph,
  title={Graph Convolutional Matrix Completion},
  author={van den Berg, Rianne and Kipf, Thomas N and Welling, Max},
  journal={arXiv preprint arXiv:1706.02263},
  year={2017}
}