Recommender Systems

In this course, participants learn the essentials of recommender systems. We start with an introduction to recommender systems and its applications. We then discuss the various ways and attention points of building a recommendation system. This is followed by an-depth discussion on collaborative filtering, one of the most popular ways of building recommender systems nowadays. We then discuss model based collaborative filtering, which relies on machine learning techniques to build recommendation systems. Next, we zoom in on matrix factorization to discover latent features in rating matrices. We then elaborate on content filtering and knowledge based filtering. We also discuss deep learning neural networks for building recommendation systems. The course concludes by reviewing recommendation system attacks, software and challenges.

The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and examples. Throughout the course, the instructors also extensively report upon their research and industry experience.

Examples are given in Python both as Jupyter notebooks and plain Python code which the participants can easily copy/paste and run in their own Python environments.

The course features more than 3.5 hours of video lectures, more than 50 multiple choice questions, and various references to background literature. A certificate signed by the instructors is provided upon successful completion.

See Singular Value Decomposition to get a free teaser of the course contents.