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Amazon Recommender System with Graph Neural Networks (GNNs)

Welcome to the Amazon Recommender System project! This repository implements a recommendation system for Amazon products using Graph Neural Networks (GNNs). The system goes beyond traditional recommendation algorithms like collaborative filtering by leveraging advanced graph-based methods to uncover deeper insights into user-product interactions.

📌 Overview

Motivation

Recommendation systems play a vital role in personalizing user experiences. Amazon's ability to recommend products effectively relies on advanced algorithms. This project explores Graph Neural Networks to simulate such a system.

Highlights

  • Built using the Amazon 2018 dataset with a subset processed using the 5-kcore method.
  • Utilizes GraphSAGE and Graph Attention Networks (GAT) for node embeddings and aggregation.
  • Benchmarked against traditional models like KNN and SVD.

🛠️ Features

  1. Graph Representation:

    • Nodes: Users and products.
    • Edges:
      • Reviews: Connect users to products with ratings.
      • Also Bought: Connect products frequently purchased together.

  2. Node Embeddings:

    • Product Nodes: Generated using sentence-transformer/all-MiniLM-L6-v2 for descriptions and one-hot encoded categories.
    • User Nodes: Initialized as blank and updated dynamically based on interactions.

  3. GNN Models:

    • GraphSAGE: Learns inductive representations of nodes.
    • GAT: Assigns attention weights to neighbors for better aggregation.

  4. Explainability:

    • Integrated gradients via Captum to interpret model predictions.

  5. Evaluation:

    • Metric: Root Mean Squared Error (RMSE).
    • Comparison against traditional methods and a state-of-the-art Graph-based Hybrid Recommendation System (GHRS).

🚀 Getting Started

Prerequisites

  • Python 3.9
  • PyTorch Geometric
  • Hugging Face Transformers
  • Other Python libraries (see requirements.txt)

Installation

  1. Clone the repository: 
    git clone https://github.com/aiden200/ARS.git
cd ARS
  2. Install the required Packages: pip install -r requirements.txt.
  3. Run the Cells in ARS.ipynb

The data is already downloaded in the repository, so there is no need to uncomment the data collection line within the notebook.

Our paper: Final_paper