Recommendation Systems
Collaborative filtering, content-based filtering, two-tower models, and building personalized recommendation engines.
Overview
Recommendation Systems power personalized experiences across the internet — from Netflix's movie suggestions to Amazon's product recommendations and Spotify's music discovery. They are one of the most commercially impactful applications of ML.
Core approaches include collaborative filtering (user-based and item-based, finding similar users/items based on interaction patterns), content-based filtering (matching item features to user preferences), matrix factorization (SVD, ALS for learning latent factors), and deep learning approaches (Two-Tower models, Neural Collaborative Filtering, sequential recommendations).
Key challenges include the cold start problem (new users/items), scalability (serving millions of users in real-time), diversity vs. relevance tradeoffs, and evaluation (offline metrics like NDCG, Precision@K vs. online A/B testing). Production systems typically use hybrid approaches combining multiple strategies.