# 10 - Recommendation Systems: Collaborative and Content-Based Filtering

Discover how recommendation systems work (Amazon, Netflix). Collaborative filtering (user-item matrix, similarity), content-based (feature similarity), hybrid approaches. Python implementation, evaluation metrics, and how to handle the cold-start problem.

## What you'll learn

- Collaborative Filtering leverages similar users; Content-Based leverages item features
- Cosine similarity is the standard metric for measuring similarity
- Matrix Factorization (SVD) decomposes the user-item matrix into latent factors
- The cold-start problem requires hybrid strategies and intelligent fallbacks
- Precision@K and NDCG are the most important metrics for evaluating recommendations

*This article is part of the **Machine Learning** series on federicocalo.dev.*

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## Read the full article

The complete article (16 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [10 - Recommendation Systems: Collaborative and Content-Based Filtering](https://federicocalo.dev/en/blog/recommendation-systems-collaborative-content-based-filtering)**

`https://federicocalo.dev/en/blog/recommendation-systems-collaborative-content-based-filtering`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*