Music Recommendation System with Reinforcement Learning

This repository contains a reinforcement learning-based music recommendation system for a music application. The system aims to learn user preferences and provide personalized song recommendations.

Table of Contents

• Project Overview
• Getting Started
  • Prerequisites
  • Installation
• Reinforcement Learning Approach
• Project Status
• Issues
• Contributing
• License
• Libraries

Project Overview

This project explores the application of reinforcement learning to build a music recommendation system. The goal is to develop an agent that can learn user preferences and provide relevant song recommendations, enhancing the user experience.

Getting Started

Prerequisites

• Python 3.11.9
• pip
• VS Code (or your preferred editor)

Installation

1. Clone the repository:

   git clone https://github.com/samuelsurr/music-recommendation-rl.git
   cd music-recommendation-rl

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On macOS/Linux
   venv\Scripts\activate  # On Windows

3. Install the required packages:

   pip install tensorflow tf-agents gym numpy pandas matplotlib librosa

Reinforcement Learning Approach

• Environment: Custom Gym environment representing the music recommendation scenario.
• Agent: Using TensorFlow Agents, potentially using DQN, DDPG, or other suitable algorithms.
• Reward Function: Designed to incentivize relevant song recommendations based on user interactions.
• State Representation: User profile, recent listening history, and song metadata.

Project Status

• Data ingestion and preprocessing.
• Gym environment setup.
• Initial RL agent implementation.
• Offline training with static dataset.
• Online training with real-time user data.
• Evaluation and performance analysis.

Known Issues

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes.
4. Commit your changes and push to your fork.
5. Submit a pull request.

License

This project is licensed under the MIT License.

Libraries Used

• TensorFlow Agents
• Gym
• NumPy
• Pandas
• Matplotlib
• Librosa