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Recommendation Engine - ML-basierte Empfehlungen mit ThemisDB

Status Difficulty Duration

📝 Übersicht

Die Recommendation Engine demonstriert ML-Integration mit ThemisDB. Sie lernen:

  • User Behavior Tracking
  • Collaborative Filtering
  • Content-Based Filtering
  • Hybrid Recommendations
  • A/B Testing
  • Real-Time Personalization

✨ Features

  • ✅ Collaborative Filtering - User-Item Matrix
  • ✅ Content-Based - Feature Similarity
  • ✅ Hybrid Approach - Beste aus beiden
  • ✅ Behavior Tracking - Clicks, Views, Purchases
  • ✅ A/B Testing - Algorithmus-Vergleich
  • ✅ Real-Time Updates - Live-Personalisierung
  • ✅ Evaluation Metrics - Precision, Recall
  • ✅ Cold Start - Für neue User/Items

📊 Datenmodell

Interaction

{
  "id": "int_uuid",
  "user_id": "user_uuid",
  "item_id": "item_uuid",
  "type": "purchase",
  "rating": 5,
  "timestamp": "2025-12-22T15:00:00Z",
  "context": {"device": "mobile", "location": "home"}
}

User

{
  "id": "user_uuid",
  "preferences": ["sci-fi", "action", "drama"],
  "demographics": {"age": 28, "gender": "f"},
  "behavior_vector": [0.8, 0.3, 0.6, ...]
}

Item

{
  "id": "item_uuid",
  "title": "Product Name",
  "category": "Electronics",
  "features": ["feature1", "feature2"],
  "embedding": [0.2, 0.7, 0.4, ...]
}

🛠️ ThemisDB Features

  • Graph für User-Item Relationships
  • Vector Search für Content-Based
  • Time-Series für Behavior Tracking
  • ML Integration für Training

🔗 Navigation

Status: Ready | Letzte Aktualisierung: 2025-12-22