Recommendation Engine

A solid starting point if you're building personalized recommendations from scratch. Covers the main approaches: collaborative filtering, content-based, matrix factorization, and hybrid methods. It's designed for typical use cases like e-commerce products, streaming content, or news feeds, and includes guidance on handling cold start problems when you don't have much user data yet. The skill also touches on evaluation metrics like precision@k and NDCG, which matter when you need to actually measure if your recommendations are working. It won't architect your entire system, but it gives you the prompting framework to get Claude implementing these algorithms instead of writing boilerplate recommendation logic yourself.

GitHub stars: 317
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill 'Recommendation Engine' --agent claude-code

Installs into .claude/skills of the current project.

Files

Recommendation Engine

Overview

This skill provides comprehensive implementation of recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and hybrid approaches to predict user preferences and deliver personalized suggestions.

When to Use

Building personalized product recommendations for e-commerce platforms
Creating content recommendation systems for streaming services, news platforms, or social media
Implementing user-user or item-item collaborative filtering based on interaction patterns
Addressing cold start problems for new users or items with limited interaction history
Evaluating recommendation quality using precision@k, recall@k, and NDCG metrics
Scaling recommendation systems to handle millions of users and items efficiently