Recommender Systems

This lecture covers the concept of recommender systems, which help match users with items by ranking them based on relevance. It explains collaborative and content-based approaches, illustrating how user-based collaborative filtering and item-based collaborative filtering work. The lecture also delves into similarity metrics between users and items, aggregation of ratings, and advanced methods like matrix factorization and stochastic gradient descent. It discusses the challenges of cold start problems, scalability, and data dispersion, offering solutions and highlighting the importance of content-based recommendations.