Graph Convolutional Matrix Completion based on Pytorch

PyTorch and PyTorch geometric based implementation of Graph Convolutional Matrix Completion.

This repository is NOT an official implementation of that paper.
The official implementation is this which is based on Tensorflow.

Train and Test

Default training dataset is MovieLens-100k which will be automatically downloaded in ./data/ when running main.py.

Docker (Recommend)

• Install docker and docker-compose (see docker official document)
• Clone this repository

git clone https://github.com/tanimutomo/gcmc.git

• Build the docker container

docker-compose -f ./docker/docker-compose-{cpu/gpu}.yml build

• Train and test the model

docker-compose -f ./docker/docker-compose-{cpu/gpu}.yml run experiment python3 main.py

Local

Installation of Pytorch Geometric is difficult and can destroy your local python environment.
So, if you already installed docker and docker-compose in your machine, recommend to use Docker (above).
Please see Pytorch Geometirc document for more details.

Install requirements

• torch==1.0.0
• torchvision
• comet_ml (you must have comet-ml account)
• torch-scatter
• torch-sparse
• torch-cluster
• torch-spline_conv
• torch-geometric