Recommender Systems: Part 1

This lecture covers recommender systems, focusing on collaborative filtering and content-based recommendation. It explains user-based and item-based collaborative filtering, similarity metrics, and aggregation functions. The instructor also discusses the cold-start problem, scalability, and matrix factorization for deriving latent factors.