Recommendation Systems: Collaborative and Content-Based

Recommendation systems power personalized experiences on platforms like Netflix, Amazon, and Spotify. This lesson covers the core algorithms.

Collaborative Filtering

User-User Collaborative Filtering

Cosine similarity between users and :

Item-Item Collaborative Filtering

Matrix Factorization (SVD)

Matrix factorization decomposes the rating matrix into low-rank factors:

where is global mean, are biases, and are latent factors.

Content-Based Filtering

Evaluation Metrics

Key Takeaways

1. Collaborative filtering uses user behavior patterns
2. Content-based filtering uses item features
3. Matrix factorization handles sparse data well
4. Hybrid systems combine both approaches
5. Use Precision@K and NDCG for evaluation