/recommendation-system

name: recommendation-system
description: Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.
license: MIT
metadata:
  keywords: "recommendation system, personalization, feature store, model serving, caching strategy, Redis, A/B testing, Thompson sampling, recommendation metrics, CTR, conversion rate, catalog coverage, diversity, Prometheus monitoring, recommendation API, real-time recommendations, collaborative filtering integration, production recommendations, experiment tracking"

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

# 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 cached:
        return RecommendationResponse(
            user_id=user_id,
            items=json.loads(cached),
            cached=True
        )

    # Generate recommendations (simplified)
    items = [f"item_{i}" for i in range(n)]

    # Cache for 5 minutes
    cache.setex(cache_key, 300, json.dumps(items))

    return RecommendationResponse(
        user_id=user_id,
        items=items,
        cached=False
    )

@app.get("/health")
async def health():
    return {"status": "healthy"}
EOF

# 4. Run API
uvicorn app:app --host 0.0.0.0 --port 8000

# 5. Test
curl -X POST "http://localhost:8000/recommendations?user_id=user_123&n=10"

**Result**: Working recommendation API with caching in under 5 minutes.

System Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│ User Events │────▶│ Feature     │────▶│ Model       │
│ (clicks,    │     │ Store       │     │ Serving     │
│  purchases) │     │ (Redis)     │     │             │
└─────────────┘     └─────────────┘     └─────────────┘
                           │                    │
                           ▼                    ▼
                    ┌─────────────┐     ┌─────────────┐
                    │ Training    │     │ API         │
                    │ Pipeline    │     │ (FastAPI)   │
                    └─────────────┘     └─────────────┘
                                               │
                                               ▼
                                        ┌─────────────┐
                                        │ Monitoring  │
                                        │ (Prometheus)│
                                        └─────────────┘

Core Components

1. Feature Store

Centralized storage for user and item features:

import redis
import json

class FeatureStore:
    """Fast feature access with Redis caching."""

    def __init__(self, redis_client):
        self.redis = redis_client
        self.ttl = 3600  # 1 hour

    def get_user_features(self, user_id: str) -> dict:
        cache_key = f"user_features:{user_id}"
        cached = self.redis.get(cache_key)

        if cached:
            return json.loads(cached)

        # Fetch from database
        features = fetch_from_db(user_id)

        # Cache
        self.redis.setex(cache_key, self.ttl, json.dumps(features))
        return features

2. Model Serving

Serve multiple models for A/B testing:

class ModelServing:
    """Serve multiple recommendation models."""

    def __init__(self):
        self.models = {}

    def register_model(self, name: str, model, is_default: bool = False):
        self.models[name] = model
        if is_default:
            self.default_model = name

    def predict(self, user_features: dict, item_features: list, model_name: str = None):
        model = self.models.get(model_name or self.default_model)
        return model.predict(user_features, item_features)

3. Caching Layer

Multi-tier caching for low latency:

class TieredCache:
    """L1 (memory) -> L2 (Redis) -> L3 (database)."""

    def __init__(self, redis_client):
        self.l1_cache = {}  # In-memory
        self.redis = redis_client  # L2

    def get(self, key: str):
        # L1: In-memory (fastest)
        if key in self.l1_cache:
            return self.l1_cache[key]

        # L2: Redis
        cached = self.redis.get(key)
        if cached:
            value = json.loads(cached)
            self.l1_cache[key] = value  # Promote to L1
            return value

        # L3: Miss (fetch from database)
        return None

Key Metrics

| Metric | Description | Target | |--------|-------------|--------| | **CTR** | Click-through rate | >5% | | **Conversion Rate** | Purchases from recs | >2% | | **P95 Latency** | 95th percentile response