An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework

This is the official implementation of the paper An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework, accepted as demo paper at UMAP 2023.

Authors: Daniele Malitesta*, Claudio Pomo*, Vito Walter Anelli, Tommaso Di Noia, Antonio Ferrara

* Corresponding authors.

Here you may find our public Docker image: https://hub.docker.com/r/sisinflabpoliba/demo-graph.

Table of Contents

• Abstract
• Pre-requisites
• Installation
• Implemented Models
• Demo Datasets
• Running Examples
• Video Tutorial
• Contacts

Abstract

Graph convolutional networks (GCNs) are taking over collaborative filtering-based recommendation. Their message-passing schema effectively distills the collaborative signal throughout the user-item graph by propagating informative content from neighbor to ego nodes. In this demonstration, we show how to run complete experimental pipelines with six state-of-the-art graph recommendation models in Elliot (i.e., our framework for recommender system evaluation). We seek to highlight four main features, namely: (i) we support reproducibility in PyTorch Geometric (i.e., the library we use to implement the baselines); (ii) reproduced graph models span across various GCN families; (iii) we allow to easily run ablation studies on the number of explored hops; (iv) we prepare a Docker image to provide a self-consistent ecosystem for the running of experiments. Codes, datasets, and a video tutorial to install and launch the application are accessible at https://github.com/sisinflab/Graph-Demo.

Pre-requisites

Before installing and running our application, please make sure you have the proper NVIDIA drivers installed on your local machine. One of the possible ways to check it (on Ubuntu 18.04) is to run the command:

nvidia-smi

If everything works smoothly, you should have a similar output:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.129.06   Driver Version: 470.129.06   CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000001:00:00.0 Off |                    0 |
| N/A   40C    P0    28W /  70W |      0MiB / 15109MiB |      4%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Installation

These are the following steps to install our application (tested on Ubuntu 18.04):

1. Install Docker Engine

Reference link: https://docs.docker.com/engine/install/ubuntu/

Commands:

sudo apt-get update

sudo apt-get install \
    ca-certificates \
    curl \
    gnupg \
    lsb-release

sudo mkdir -p /etc/apt/keyrings

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

sudo apt-get update

sudo apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin

2. Install NVIDIA Container Toolkit

Reference link: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#setting-up-nvidia-container-toolkit

Commands:

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

sudo apt-get update

sudo apt-get install -y nvidia-docker2

sudo systemctl restart docker

sudo docker run --rm --gpus all nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi

Expected output:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06    Driver Version: 450.51.06    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            On   | 00000000:00:1E.0 Off |                    0 |
| N/A   34C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

3. Install Docker Compose

Reference link: https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04

Commands:

mkdir -p ~/.docker/cli-plugins/

curl -SL https://github.com/docker/compose/releases/download/v2.6.0/docker-compose-linux-x86_64 -o ~/.docker/cli-plugins/docker-compose

chmod +x ~/.docker/cli-plugins/docker-compose

docker compose version

Expected output:

Docker Compose version v2.6.0

Implemented Models

Models	Title	Authors	Year	Conference	Link DOI
NGCF	Neural Graph Collaborative Filtering	Wang et al.	2019	SIGIR	https://dl.acm.org/doi/10.1145/3331184.3331267
DGCF	Disentangled Graph Collaborative Filtering	Wang et al.	2020	SIGIR	https://dl.acm.org/doi/abs/10.1145/3397271.3401137
LightGCN	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	He et al.	2020	SIGIR	https://dl.acm.org/doi/10.1145/3397271.3401063
SGL	Self-supervised graph learning for recommendation	Wu et al.	2021	SIGIR	https://doi.org/10.1145/3404835.3462862
UltraGCN	UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation	Mao et al.	2021	CIKM	https://dl.acm.org/doi/10.1145/3459637.3482291
GFCF	How Powerful is Graph Convolution for Recommendation?	Shen et al.	2021	CIKM	https://dl.acm.org/doi/abs/10.1145/3459637.3482264

Demo Datasets

Datasets	Users	Items	Interactions	Official Link	Download Link
Gowalla	29,858	40,981	1,027,370	https://snap.stanford.edu/data/loc-gowalla.html	link
Yelp 2018	31,668	38,048	1,561,406	https://www.yelp.com/dataset	link
Amazon Book	52,643	91,599	2,984,108	https://jmcauley.ucsd.edu/data/amazon/	link

Running Examples

Here, we provide two possible examples to run and test our application.

First, let us clone this GitHub repository so that we can access to the YAML docker compose file.

git clone https://github.com/sisinflab/Graph-Demo.git

cd Graph-Demo/

1. Train and evaluate NGCF (explicit message-passing)

sudo docker compose run demo-graph

Then, select:

• Model: ngcf
• Dataset: gowalla

2. Train and evaluate GFCF (no message-passing)

sudo docker compose run demo-graph

Then, select:

• Model: gfcf
• Dataset: gowalla

Video Tutorial

If you need a practical guide to install and launch our application, click on the image below to go to the video tutorial on YouTube.

Video link: https://www.youtube.com/watch?v=_Bpgf4wnwIU

Contacts

• Daniele Malitesta ([email protected])
• Claudio Pomo ([email protected])
• Vito Walter Anelli ([email protected])
• Tommaso Di Noia ([email protected])
• Antonio Ferrara ([email protected])