Recommendation System

If you're building personalized product suggestions or trying to boost engagement through recommendations, this handles both collaborative and content-based filtering with matrix factorization under the hood. It's designed for the practical stuff: dealing with sparse interaction data, cold start problems, and running A/B tests to see if your recommendations actually move metrics. The skill covers hybrid approaches when you need to combine multiple techniques. It's from aj-geddes' useful-ai-prompts collection, which has 245 GitHub stars and passes security audits. Good fit if you're implementing recommendation features and want guidance on handling the common edge cases that trip people up.

GitHub stars: 317
last updated: Aug 21, 2026

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Install

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

Installs into .claude/skills of the current project.

Files

Recommendation System

Overview

This skill implements collaborative and content-based recommendation systems with matrix factorization techniques to predict user preferences, increase engagement, and drive conversions through personalized item suggestions.

When to Use

Developing recommendation features to improve user engagement and retention
Implementing personalized product suggestions to increase sales and conversion rates
Building hybrid recommendation systems that combine collaborative and content-based approaches
Analyzing and optimizing recommendation coverage, diversity, and accuracy
Handling sparse user-item interaction matrices and cold start scenarios
Running A/B tests to measure the impact of recommendation algorithms on business metrics