The key points covered in this text are that machine learning models for recommender systems can be categorized into three main types: content-based filtering, collaborative filtering, and hybrid approaches. Content-based filtering recommends items based on their attributes, such as tags or descriptions, while collaborative filtering uses user ratings to predict preferences. The similarity between users is measured using the cosine similarity formula, which calculates the dot product of two vectors divided by their magnitude. This approach can be used for both item-based and user-based collaborative filtering. Hybrid approaches combine one or more techniques to create a personalized recommendation system. Factorization machines are a type of hybrid model that uses structured data to estimate unknown ratings. The choice of approach depends on the specific use case, such as eCommerce or media companies, and scalability considerations.