plugins-recommendation-system-skills-recommendation-system

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SKILL.md

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"

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

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

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)

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

Metric	Description	Target
CTR	Click-through rate	>5%
Conversion Rate	Purchases from recs	>2%
P95 Latency	95th percentile response time	<200ms
Cache Hit Rate	% served from cache	>80%
Coverage	% of catalog recommended	>50%
Diversity	Variety in recommendations	>0.7

def get_recommendations(user_id: str, n: int = 10):
    user_features = feature_store.get_user_features(user_id)

    # Check if new user (no purchase history)
    if user_features.get('total_purchases', 0) == 0:
        # Fallback to popular items
        return get_popular_items(n)

    # Personalized recommendations
    return generate_personalized_recs(user_id, n)

INVALIDATING_ACTIONS = {'purchase', 'rating', 'add_to_cart'}

def on_user_action(user_id: str, action: str):
    if action in INVALIDATING_ACTIONS:
        cache_key = f"recs:{user_id}:*"
        redis_client.delete(cache_key)
        logger.info(f"Invalidated cache for {user_id} due to {action}")

import random

def set_cache(key: str, value: dict, base_ttl: int = 300):
    # Add ±10% jitter
    jitter = random.uniform(-0.1, 0.1) * base_ttl
    ttl = int(base_ttl + jitter)
    redis_client.setex(key, ttl, json.dumps(value))

def rank_with_diversity(items: list, scores: list, n: int = 10):
    selected = []
    category_counts = {}

    for item, score in sorted(zip(items, scores), key=lambda x: -x[1]):
        category = item['category']

        # Limit 3 items per category
        if category_counts.get(category, 0) >= 3:
            continue

        selected.append(item)
        category_counts[category] = category_counts.get(category, 0) + 1

        if len(selected) >= n:
            break

    return selected

from prometheus_client import Counter, Histogram

recommendation_clicks = Counter('recommendation_clicks_total')
recommendation_latency = Histogram('recommendation_latency_seconds')

@app.post("/recommendations")
async def get_recommendations(user_id: str):
    start = time.time()

    recs = generate_recs(user_id)

    latency = time.time() - start
    recommendation_latency.observe(latency)

    return recs

@app.post("/track/click")
async def track_click(user_id: str, item_id: str):
    recommendation_clicks.inc()
    # Alert if CTR drops below 3%

class FeatureStore:
    def __init__(self, redis_client):
        self.redis = redis_client
        # Shorter TTL for frequently changing features
        self.user_ttl = 300  # 5 minutes
        self.item_ttl = 3600  # 1 hour

    def update_on_event(self, user_id: str, event: str):
        # Invalidate on important events
        if event in ['purchase', 'rating']:
            self.redis.delete(f"user_features:{user_id}")
            logger.info(f"Refreshed features for {user_id}")

def calculate_sample_size(
    baseline_rate: float,
    min_detectable_effect: float,
    alpha: float = 0.05,
    power: float = 0.8
) -> int:
    """Calculate required sample size per variant."""
    from scipy import stats

    z_alpha = stats.norm.ppf(1 - alpha/2)
    z_beta = stats.norm.ppf(power)

    p1 = baseline_rate
    p2 = baseline_rate * (1 + min_detectable_effect)
    p_avg = (p1 + p2) / 2

    n = (
        (z_alpha + z_beta)**2 * 2 * p_avg * (1 - p_avg) /
        (p2 - p1)**2
    )

    return int(n)

# Example: detect 10% lift with baseline CTR=5%
n_required = calculate_sample_size(
    baseline_rate=0.05,
    min_detectable_effect=0.10
)
print(f"Required sample size: {n_required} per variant")
# Wait until both variants reach this size before concluding

class RecommendationService:
    def __init__(self, feature_store, model_serving, cache):
        self.feature_store = feature_store
        self.model_serving = model_serving
        self.cache = cache

    def get_recommendations(self, user_id: str, n: int = 10):
        # 1. Check cache
        cached = self.cache.get(f"recs:{user_id}:{n}")
        if cached:
            return cached

        # 2. Get features
        user_features = self.feature_store.get_user_features(user_id)
        candidates = self.get_candidates(user_id)

        # 3. Score candidates
        scores = self.model_serving.predict(user_features, candidates)

        # 4. Rank with diversity
        recommendations = self.rank_with_diversity(candidates, scores, n)

        # 5. Cache
        self.cache.set(f"recs:{user_id}:{n}", recommendations, ttl=300)

        return recommendations

def assign_variant(user_id: str, experiment_id: str) -> str:
    """Deterministic assignment - same user always gets same variant."""
    import hashlib

    hash_input = f"{user_id}:{experiment_id}"
    hash_value = int(hashlib.md5(hash_input.encode()).hexdigest(), 16)

    # 50/50 split
    return 'control' if hash_value % 2 == 0 else 'treatment'

# Usage
variant = assign_variant('user_123', 'rec_algo_v2')
model_name = 'main' if variant == 'control' else 'experimental'
recs = get_recommendations(user_id, model_name=model_name)

from prometheus_client import Counter, Histogram

requests_total = Counter('recommendation_requests_total', ['status'])
latency_seconds = Histogram('recommendation_latency_seconds')

@app.post("/recommendations")
async def get_recommendations(user_id: str):
    with latency_seconds.time():
        try:
            recs = generate_recs(user_id)
            requests_total.labels(status='success').inc()
            return recs
        except Exception as e:
            requests_total.labels(status='error').inc()
            raise

plugins-recommendation-system-skills-recommendation-system

Summary

SKILL.md

Recommendation System

When to Use This Skill

Quick Start: Recommendation API in 5 Steps

System Architecture

Core Components

1. Feature Store

2. Model Serving

3. Caching Layer

Key Metrics

Known Issues Prevention

1. Cold Start for New Users

2. Cache Invalidation on User Actions

3. Thundering Herd on Cache Expiry

4. Poor Diversity = Filter Bubble

5. No Monitoring = Silent Degradation

6. Stale Features = Outdated Recommendations

7. A/B Test Sample Size Too Small

When to Load References

Best Practices

Common Patterns

Recommendation Service

A/B Testing

Monitoring

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