In this course, you will see how to use advanced machine learning techniques in order to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model.

At the end of this course, you will learn how to manage hybrid information and how to combine different filtering techniques, taking the best from each approach. You will know how to use factorization machines and represent the input data accordingly. You will be able to design more sophisticated recommender systems, which can solve the cross-domain recommendation problem. You will also learn how to identify new trends and challenges in providing recommendations in a range of innovative application contexts. This course leverages two important EIT Overarching Learning Outcomes (OLOs), related to your creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the outcomes. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and solve real-life problems in complex and innovative scenarios.