Recommender Systems: Under the Hood of Recommendation Systems

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Users marvel at a system's ability to recommend items they're likely to appreciate. As someone working with machine learning, implementing these recommendation systems (also called recommender systems) can dramatically increase user engagement and goodwill towards your products or brand. Use this course to comprehend the math behind recommendation systems and how to apply latent factor analysis to make recommendations to users. Examine the intuition behind recommender systems before investigating two of the main techniques used to build them: content-based filtering and collaborative filtering. Moving on, explore latent factor analysis by decomposing a ratings matrix into its latent factors using the gradient descent algorithm and implementing this technique to decompose a ratings matrix using the Python programming language. By the end of this course, you'll be able to build a recommendation system model that best suits your products and users.