Reinforcement Learning with Human Feedback (RLHF) for Movie Recommendations

This project demonstrates how to apply Reinforcement Learning (RL) combined with Human Feedback (RLHF) to optimize a movie recommender system. By using human feedback, the system continuously learns from users' interactions and improves its recommendations over time.

Table of Contents

1. Introduction
2. Technologies Used
3. Project Structure
4. Installation Guide
5. How to Use
6. Model Training
7. Evaluation
8. Contributing
9. License
10. Acknowledgements

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Introduction

In this project, we build a recommender system based on the MovieLens dataset. We implement Reinforcement Learning techniques, such as Proximal Policy Optimization (PPO), to optimize the movie recommendation process. The recommender system integrates Human Feedback to improve over time based on users' ratings and choices.

This system is an example of applying RL and RLHF in a production-grade environment, allowing for dynamic recommendations and personalized user experiences.

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Technologies Used

• Reinforcement Learning: PPO (Proximal Policy Optimization), Q-learning
• Human Feedback Integration: User ratings and preferences to improve recommendations
• NLP: Preprocessing of user feedback using basic NLP techniques
• Deep Learning: TensorFlow and Keras for model training
• Data Handling: Pandas for data processing and management
• Visualization: Matplotlib, Seaborn for visualizing model performance and recommendations
• Deployment: Flask (for API deployment), Docker for containerization

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Project Structure

movie-recommender/
├── data/
│   └── movielens_data.csv          # The MovieLens dataset
├── models/
│   └── recommendation_model.h5     # The trained recommendation model
├── notebooks/
│   ├── 01_data_exploration.ipynb   # Data exploration notebook
│   ├── 02_data_preprocessing.ipynb # Data preprocessing notebook
│   ├── 03_model_training.ipynb     # Model training notebook
│   ├── 04_reward_model_training.ipynb  # Training the RL reward model
│   ├── 05_rl_agent_training.ipynb  # Reinforcement Learning agent training
│   └── 06_evaluation_and_analysis.ipynb # Model evaluation notebook
├── src/
│   ├── data_loader.py              # Code to load and preprocess data
│   ├── model.py                    # The recommendation model code
│   ├── rl_agent.py                 # RL agent for recommendation
│   ├── feedback_system.py          # Human feedback integration
│   └── utils.py                    # Utility functions
├── config/
│   └── config.py                   # Configuration file for hyperparameters
├── app/
│   ├── app.py                      # Flask app for serving the model
│   └── requirements.txt            # Python dependencies
├── logs/
│   └── training_logs.txt           # Logs for model training and evaluation
└── README.md                       # This file