Can I run experiments with different recommendation models?

Yes, the skill includes a Model Serving component and an A/B testing framework that supports deterministic variant assignment and significance testing.

Is this skill compatible with modern Python stacks?

Yes, the provided implementation patterns use FastAPI, Redis-py, and Prometheus-client, making it ideal for modern Python-based microservices.

How does this skill handle high latency in recommendations?

It implements a tiered caching strategy using L1 in-memory storage and L2 Redis caching to serve recommendations with sub-millisecond response times.

What metrics are tracked by the monitoring system?

It integrates with Prometheus to monitor Click-Through Rate (CTR), conversion rates, P95 latency, catalog coverage, and recommendation diversity.

Does it address the cold start problem for new users?

Yes, the skill provides implementation patterns for popularity-based fallbacks to ensure a good user experience when historical data is unavailable.

Recommendation System Engineering

Name: Recommendation System Engineering
Author: secondsky

bysecondsky

•

Data Science & ML

Deploys production-ready recommendation architectures featuring multi-tier caching, feature stores, and automated A/B testing frameworks.

About

This skill provides a comprehensive blueprint for building and scaling high-performance recommendation engines within the Claude Code environment. It bridges the gap between raw machine learning models and production APIs by implementing essential infrastructure components like Redis-backed feature stores, L1/L2 caching strategies, and robust monitoring for CTR and diversity metrics. Whether you are addressing cold-start problems, thundering herd issues, or complex A/B testing scenarios, this skill offers production-grade patterns to ensure sub-millisecond latency and high-quality user personalization for e-commerce and content platforms.

Key Features

11 GitHub stars
Feature store implementation for real-time user and item attribute management
A/B testing framework with Thompson sampling and statistical significance logic
Quality monitoring for CTR, conversion, diversity, and catalog coverage
Automated handling of common issues like cold starts and cache invalidation
Multi-tier caching (L1/Redis) for sub-millisecond recommendation latency

Use Cases

Scaling personalization APIs for high-traffic e-commerce or content platforms
Optimizing backend performance and reducing database load through advanced caching
Implementing experimental frameworks to test and validate new recommendation algorithms

About

Key Features

11 GitHub stars
Feature store implementation for real-time user and item attribute management
A/B testing framework with Thompson sampling and statistical significance logic
Quality monitoring for CTR, conversion, diversity, and catalog coverage
Automated handling of common issues like cold starts and cache invalidation
Multi-tier caching (L1/Redis) for sub-millisecond recommendation latency

Use Cases

Scaling personalization APIs for high-traffic e-commerce or content platforms
Optimizing backend performance and reducing database load through advanced caching
Implementing experimental frameworks to test and validate new recommendation algorithms