A personalized content aggregator that fetches and curates content from various online sources based on user preferences.

• Content aggregation from RSS feeds, X/Twitter, and social media
• Personalized content recommendations using machine learning (LightGBM)
• User interaction tracking for improved recommendations
• Privacy controls for user data
• Clean, responsive user interface

• Backend: Node.js, Express
• Frontend: React.js
• Databases: MongoDB (content storage), PostgreSQL (user data)
• Recommendation Engine: Python with LightGBM (Machine Learning)

• Node.js (v14+)
• Python 3.7+
• MongoDB
• PostgreSQL

1. Clone the repository

git clone <repository-url>
cd GlovePost

2. Set up the backend

cd backend
npm install

3. Set up the frontend

cd frontend/glovepost-ui
npm install

4. Configure environment variables

Edit the .env file in the backend directory to match your database settings:

PORT=3000
MONGO_URI=mongodb://localhost:27017/glovepost
PG_URI=postgres://user:password@localhost:5432/glovepost

5. Run the setup scripts

# General setup
cd scripts
chmod +x run_setup.sh
./run_setup.sh

# Set up ML environment (optional)
chmod +x setup_ml_env.sh
./setup_ml_env.sh

These scripts will:

• Create necessary database tables
• Set up Python environment
• Fetch initial content
• Set up the machine learning environment (if running setup_ml_env.sh)

1. Start the backend server

cd backend
npm run dev

2. Start the frontend development server

cd frontend/glovepost-ui
npm start

3. Access the application

Open your browser and navigate to http://localhost:3001

• backend/server.js - Main Express application
• backend/routes/ - API routes
• backend/models/ - Database models

• frontend/glovepost-ui/src/components/ - React components
• frontend/glovepost-ui/src/services/ - API communication

• scripts/content_aggregator.py - Fetches content from sources
• scripts/refresh_content.py - Multithreaded content scraping system
• scripts/recommendation_engine.py - Generates basic personalized recommendations
• scripts/ml_recommendation_engine.py - Machine learning-based recommendation system
• scripts/test_ml_recommendation.py - Unit tests for the ML recommendation engine

For more details on the ML recommendation system, see ML_RECOMMENDATIONS.md

For more details on the multithreaded scraper, see SCRAPER_README.md

• GET /content/latest - Get latest content

• GET /user/:id - Get user information
• POST /user/preferences - Update user preferences
• POST /user/consent - Update tracking consent

• GET /recommendations/:userId - Get personalized recommendations
  • Add ?ml=true query parameter to use the ML-based engine
• POST /recommendations/train - Train the ML recommendation model

• POST /interaction/track - Track user interaction with content
• GET /interaction/:userId - Get user interaction history
• DELETE /interaction/:userId - Clear user interaction history

MIT