What is a recommendation system?

A recommendation system predicts user preferences to suggest relevant items. Main approaches: Collaborative Filtering (CF) — uses patterns from many users' behavior; user-based CF finds similar users, item-based CF finds similar items, Matrix Factorization (SVD, ALS) decomposes the user-item matrix. Content-Based Filtering — recommends items similar to what the user liked based on item features. Hybrid systems combine both. Deep learning-based systems use embeddings and two-tower models. Challenges: the cold start problem (new users/items), sparsity, and scalability.