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Recommendation Engine

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Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches

skills/recommendation-engine/SKILL.md

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# 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
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