ravindramohith / movie_recommender_system

A movie recommendation system utilizing a Graph Neural Network (GNN) framework implemented in Jupyter Notebook

Movie Recommender System

This repository contains the implementation of an advanced movie recommender system using LightGCN in PyTorch Geometric (PyG). The system integrates message passing and embeddings from a bipartite graph of users and movies, utilizing both supervised and self-supervised learning techniques to enhance recommendation quality.

Features

• Graph Neural Networks: Built using LightGCN in PyG, integrating message passing and embeddings.
• Learning Methods: Utilizes both supervised and self-supervised learning to improve recommendation quality.
• Loss Functions: Trained using BPR (Bayesian Personalized Ranking) and RMSE (Root Mean Squared Error) loss functions.
• Optimizer: Uses Adam optimizer with learning rate decay for optimized convergence.
• Data Processing: Efficiently processes and evaluates graph data with sparse matrices and edge indices.
• Evaluation Metrics: Uses recall, precision, and NDCG (Normalized Discounted Cumulative Gain) metrics for evaluation.

Installation

1. Clone the repository:

   git clone https://github.com/ravindramohith/movie_recommender_system.git

2. Navigate to the project directory:

   cd movie_recommender_system

Usage

• Supervised Learning:

  • The supervised learning implementation can be found in recommender-system-using-supervised-gnn_modified.ipynb.
  • Follow the notebook to train and evaluate the supervised recommender system.

• Self-Supervised Learning:

  • The self-supervised learning implementation can be found in recommender-system-using-self-supervised-gnn_modified.ipynb.
  • Follow the notebook to train and evaluate the self-supervised recommender system.

Evaluation

The model performance is evaluated using the following metrics:

• Recall: Measures the ability of the recommender system to capture relevant items.
• Precision: Measures the accuracy of the recommended items.
• NDCG: Measures the ranking quality of the recommendations.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Contact

For any questions or suggestions, please contact me through [e-mail].