https://github.com/sephiroth7712/movie-recommender
Full-stack movie recommendation platform with personalized suggestions based on user ratings, machine learning-based genre prediction, and similar content discovery.
https://github.com/sephiroth7712/movie-recommender
fastapi fastapi-sqlalchemy knn movie-recommendation-app python reactjs recommendation-system svm
Last synced: 3 months ago
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Full-stack movie recommendation platform with personalized suggestions based on user ratings, machine learning-based genre prediction, and similar content discovery.
- Host: GitHub
- URL: https://github.com/sephiroth7712/movie-recommender
- Owner: sephiroth7712
- Created: 2024-11-15T21:43:27.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-06T19:39:01.000Z (6 months ago)
- Last Synced: 2025-02-18T23:32:13.773Z (3 months ago)
- Topics: fastapi, fastapi-sqlalchemy, knn, movie-recommendation-app, python, reactjs, recommendation-system, svm
- Language: Python
- Homepage:
- Size: 163 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Movie Genre Classification and Recommendation System
A full-stack application that combines automated movie genre classification with personalized movie recommendations. The system uses machine learning to analyze movie plots and predict genres, while offering both content-based and collaborative filtering approaches for movie recommendations.
## Features
### Genre Classification
- Automated multi-label genre classification based on movie plot summaries
- TF-IDF vectorization and Support Vector Machines for classification
- Real-time genre predictions with confidence scores
- Support for multiple genres per movie
### Movie Recommendations
- Hybrid recommendation system combining:
- Content-based filtering using movie plots and metadata
- Collaborative filtering based on user ratings
- Personalized recommendations based on user watch history
- Similar movie suggestions
- Configurable recommendation parameters
### User Features
- User authentication and profile management
- Personal watchlist
- Movie rating system
- Search functionality
- Detailed movie information display
## Technical Stack
### Backend
- FastAPI for the REST API
- SQLAlchemy for database ORM
- PostgreSQL for data storage
- Python-based machine learning models:
- scikit-learn for classification
- NLTK and spaCy for text processing
- NumPy and Pandas for data manipulation
### Frontend
- React with TypeScript
- Tailwind CSS for styling
- React Query for data fetching
- React Router for navigation
- Zustand for state management
## Setup Instructions
### Prerequisites
- Python 3.8+
- Node.js 16+
- PostgreSQL 13+
- Git
### Backend Setup
1. Clone the repository:
```bash
git clone [repository-url]
cd movie-recommender
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
python -m spacy download en_core_web_sm
```
4. Set up environment variables:
Create a `.env` file in the backend directory with:
```
DATABASE_USER=your_db_user
DATABASE_PASSWORD=your_db_password
DATABASE_DOMAIN=localhost
DATABASE_NAME=movie_recommender
```
5. Initialize the database:
```bash
python data_processing/preprocessing/preprocessing.py
python backend/data_loader.py
```
### Frontend Setup
1. Navigate to the frontend directory:
```bash
cd frontend
```
2. Install dependencies:
```bash
npm install
```
3. Create a `.env` file:
```
VITE_API_URL=http://localhost:8000
```
4. Start the development server:
```bash
npm run dev
```
## Usage Examples
### Genre Classification
```python
from genre_prediction_service import GenrePredictionService
# Initialize the service
service = GenrePredictionService()
# Predict genres for a movie plot
plot = "A police officer must save hostages from a building taken over by terrorists."
genres = service.predict_genres(plot)
```
### Movie Recommendations
```python
from recommendation_service import RecommendationService
# Initialize the service
service = RecommendationService()
# Get recommendations for a user
recommendations = await service.get_user_recommendations(
user_id=1,
n_recommendations=5,
min_rating=3.5
)
```
## API Endpoints
### Authentication
- `POST /login/` - User login
- `POST /users/` - User registration
### Movies
- `GET /movies/{movie_id}` - Get movie details
- `GET /search/movies` - Search movies
- `GET /movies/{movie_id}/recommendations` - Get similar movies
### Recommendations
- `GET /users/{user_id}/recommendations` - Get personalized recommendations
### Genre Classification
- `POST /classify` - Predict genres for a plot summary
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## Acknowledgments
- MovieLens dataset for movie data and ratings
- CMU Movie Summary Corpus for plot summaries
- IMDb for additional movie metadata