Ai powered event

[LaGodxy]

Description

✅ AI Recommendations System Implementation Complete
This PR introduces a comprehensive AI-powered recommendation system for the Veritix backend, delivering real-time personalized event suggestions, advanced analytics, and scalable architecture.
The system integrates directly into the main application, enabling enterprise-grade personalization that will significantly boost user engagement and conversion rates.

Related Issues

Closes #324

Changes Made

• Core Components Delivered

  • Entities

    • UserInteraction – Tracks all user behavior and events
    • UserPreference – Stores learned preferences with confidence scoring
    • RecommendationAnalytics – Tracks performance metrics and analytics
    • ABTestResult – A/B testing framework for optimization

  • Services

    • RecommendationEngineService – Core recommendation logic with multiple algorithms
    • MLModelService – External ML API integration for predictions
    • UserBehaviorTrackingService – Real-time user interaction tracking
    • RecommendationAnalyticsService – Analytics and performance monitoring
    • PersonalizationService – User-specific content personalization

  • Controllers

    • RecommendationsController – Public API for recommendations and tracking
    • RecommendationAnalyticsController – Admin analytics and insights

• Key Capabilities Implemented

  • ✅ Personalized Recommendations – AI-driven event suggestions based on user behavior
  • ✅ Real-Time Tracking – Automatic learning from user interactions
  • ✅ Multi-Algorithm Engine – Collaborative filtering, content-based, and hybrid approaches
  • ✅ Advanced Analytics – Performance metrics, A/B testing, and engagement insights
  • ✅ Scalable Architecture – Supports millions of users and events

• Integration & Configurations

  • Module integrated into AppModule
  • Environment configuration with ML API settings
  • Unit tests created for all major components

• Environment Variables Added

  ML_API_URL=http://localhost:8000  
  ML_API_KEY=your_ml_api_key_here  
  ML_MODEL_VERSION=v1.0  
  RECOMMENDATION_CACHE_TTL=3600  
  RECOMMENDATION_BATCH_SIZE=100  
  AB_TEST_ENABLED=true  
  RECOMMENDATION_ANALYTICS_ENABLED=true  

How to Test

1. Set environment variables as shown above.
2. Run DB migrations to create new entities (UserInteraction, UserPreference, etc.).
3. Start the server and hit recommendation endpoints in RecommendationsController.
4. Simulate user interactions → verify preferences are updated in real-time.
5. Validate recommendations include both collaborative and content-based results.
6. Access RecommendationAnalyticsController → confirm A/B test results and performance metrics.
7. Run unit tests → confirm all pass with high coverage.

Screenshots (if applicable)

N/A (API endpoints only).

Checklist

• Code follows project coding standards
• Changes tested locally with sample interactions
• Documentation updated (README.md)
• Unit tests added with full coverage
• Environment variables configured