Recommender systems attempt to predict a user's preferences to recommend relevant items. They use algorithms like collaborative filtering on user data to deliver personalized recommendations. To scale, techniques like clustering users and dimensionality reduction are used. Learning to rank models combine user preferences, item attributes, and business goals to generate a ranked list of recommendations. Open source datasets and frameworks can help get started with implementing recommender systems.