Raccomender engines use content-based and collaborative filtering approaches to make recommendations. Content-based filtering recommends items based on a comparison of item characteristics to a user's profile, while collaborative filtering builds models from users' past behaviors and preferences to identify similar users and predict interests. The document then provides examples of how matrix factorization models are used for both content-based and collaborative filtering to learn latent representations of users and items from rating data through iterative optimization.