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.

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

By Federico Calò — Software Developer & Technical Writer