Example 19 recommendation engine

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Build: cmake --preset linux-ninja-release && cmake --build --preset linux-ninja-release

Recommendation Engine - ML-basierte Empfehlungen mit ThemisDB

📝 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

• ⬅️ 18 - Real-Time Chat
• ➡️ 20 - Smart Home
• 🏠 Übersicht

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

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