Recommendation Systems

Recommendation Approaches

Recommender systems suggest items to users.

Collaborative Filtering

User-based: similar users get similar recommendations. Item-based: similar items.

Matrix factorization: SVD, NMF decomposes user-item matrix.

Content-Based

Use item features. User preferences from historical interactions. Works for new items.

Hybrid Methods

Combine collaborative and content-based. Netflix Prize winners used hybrid.

Surprise, implicit libraries implement recommendation algorithms.

Key Takeaways

1. Collaborative filtering uses user-item interactions
2. Content-based uses item features
3. Hybrid combines both approaches