This is a high-level list of topics covered in this course. Please see the detailed Agenda below • How to understand users and their behavior, and covers ways to collect data from users. • Introduces web analytics and shows how you can implement a dashboard where you can keep track of your recommenders. • How behavioral data can be transformed into ratings. • Outlines the problem of new users and products and gives simple solutions. • Discusses formulas for calculating similarity between users or content items such as movies. • presents a way to mix types of recommenders. • Introduces ranking algorithms and methods for learning to rank recommendations. • looks at non-personalized recommendations.
Are you envious when Amazon recommends its products or when Netflix is spot-on with a recommendation for a user? Then here’s your chance to learn how to add these skills to your repertoire. Reading this course will give you an understanding of what recommender systems are and how to apply them in practice. To make a recommender work, many things need to perform in concert. You need to understand how to collect data from your users and how to interpret it, and you need a toolbox of different recommender algorithms so you can choose the best one for your particular scenario. Most importantly, you need to understand how to evaluate whether your recommender system is doing its job well. All this and more is hidden within this course. Working in a hands-on learning environment, led by our Recommendation Systems expert instructor, students will learn about and explore: • you should be able to read code in a programming language such as Python or Java • you should understand an SQL query, and you should have a basic understanding of higher math and statistics. • Figures and code listings that explain concepts can get you only so far.