ExploringGraphRecommendationSystems

COMP4740_FinalProject

Members are George Kaceli and Noah Adams

In this project we will be exploring using graphs from item recommendation with an application to movie recommendation using the MovieLens100K dataset, we will utilise Graph Neural Networks, such as Graph Convolution Networks GCNs, Graph sample and aggregate GraphSAGE, and Graph Attention Networks GATs

Project Structure

Directories

• models/: Contains definitions for various graph-based neural network models.
• datasets/: utilities for managing and preprocessing the dataset used in the models.
• visualization/: Tools for visualizing data insights and model performance.
• metrics/: Utilities for calculating and logging performance metrics like precision, recall, and RMSE.

Key Files

• convert.py: Converts data into the required format for model processing.
• data.py: Manages data loading and preprocessing workflows.
• gat_train_test.py: Implements the training and testing phases for the Graph Attention Network model.
• gcn_train_test.py: Manages the training and testing processes for the Graph Convolutional Network.
• graph.py: Provides functionalities for graph data manipulation and visualization.
• log_to_csv.py: Utility to log various performance metrics to a CSV file.
• SAGE_train_test.py: Script for training and testing the GraphSAGE model.

Installation and Python Environment Setup

To get started with this project, follow these steps to set up your environment:

for Linux or macOS:

Create a virtual environment named 'venv'

python3 -m venv venv

Activate the virtual environment

source venv/bin/activate

for Windows:

Create a virtual environment named 'venv'

python -m venv venv

Activate the virtual environment

venv\Scripts\activate

bash

Clone the repository

git clone <repository-url> cd <repository-directory>

Install the necessary dependencies

pip install -r requirements.txt

Usage

To train and test the GCN model

python gcn_train_test.py

To train and test the GAT model

python gat_train_test.py

To train and test the GraphSAGE model

python SAGE_train_test.py

Table of Contents for document

0. Abstract

I. Introduction

II. Literature Review

a. Graph Convolution Networks

b. Graph SAGE

c. Graph Attention Networks

III. Methodology

a. Data Preprocessing

b. Graph Recommender Systems

c. GCN Implementation

d. Graph Sage Implementation

e. GAT Implementation

IV. Results

V. Discussion

VI. Conclusion

VII. References

VIII. Appendix

IX. Group Member Participation

more notes

Group Members. George Kaceli and Noah Adams