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ML Service Overview

The ML Service is a Python-based FastAPI application that powers the recommendation engine for the MLContext platform.

🎯 Core Functionality​

Recommendation Engine​

  • 8-Factor Algorithm: Advanced recommendation logic
  • LightFM Integration: Collaborative filtering implementation
  • Real-time Processing: Fast recommendation generation
  • Scalable Architecture: Built for production workloads

Key Features​

  • Personalized Recommendations: User-specific movie suggestions
  • Group Consensus: Multi-user recommendation aggregation
  • Similarity Matching: Content-based filtering
  • Performance Optimization: Efficient algorithm implementation

🛠️ Technology Stack​

Core Framework​

  • FastAPI: Modern Python web framework
  • LightFM: Machine learning library
  • Pandas/NumPy: Data processing
  • Scikit-learn: Additional ML utilities

Data Processing​

  • TMDB Integration: Movie metadata processing
  • Supabase Connectivity: Database integration
  • Real-time Updates: Live recommendation updates
  • Caching Strategy: Performance optimization

🚀 Getting Started​

This service integrates with the DAGGH frontend to provide intelligent movie recommendations based on user preferences and behavior.

For detailed setup instructions, see the Setup Guide.

📚 Documentation Sections​

  • LLM Context: AI-specific development context
  • General Context: Overview and setup information
  • Algorithms: Detailed algorithm documentation
  • Deployment: Production deployment guides
  • Versioning: Release history and changes

Ready to dive deeper? Explore the Architecture Guide or check out the Algorithm Documentation.