recommendation-system

# Recommendation System Production-ready architecture for scalable recommendation systems with feature stores, multi-tier caching, A/B testing, and comprehensive monitoring. ## When to Use This Skill Load this skill when: - **Building Recommendation APIs**: Serving personalized recommendations at scale - **Implementing Caching**: Multi-tier caching for sub-millisecond latency - **Running A/B Tests**: Experimenting with recommendation algorithms - **Monitoring Quality**: Tracking CTR, conversion, diversity, coverage - **Optimizing Performance**: Reducing latency, increasing throughput - **Feature Engineering**: Managing user/item features with feature stores ## Quick Start: Recommendation API in 5 Steps ```bash # 1. Install dependencies pip install fastapi==0.109.0 redis==5.0.0 prometheus-client==0.19.0 # 2. Start Redis (for caching and feature store) docker run -d -p 6379:6379 redis:alpine # 3. Create recommendation service: app.py cat > app.py << 'EOF' from fastapi import FastAPI from pydantic import BaseModel from typing import List import redis import json app = FastAPI() cache = redis.Redis(host='localhost', port=6379, decode_responses=True) class RecommendationResponse(BaseModel): user_id: str items: List[str] cached: bool @app.post("/recommendations", response_model=RecommendationResponse) async def get_recommendations(user_id: str, n: int = 10): # Check cache cache_key = f"recs:{user_id}:{n}" cached = cache.get(cache_key) if cache...