This book shows you how to do just that. You will learn about different kinds of
recommenders used in the industry and see how to build them from scratch using Python.
No need to wade through tons of linear algebra and machine learning theory, you'll get
started with building and learning about recommenders as quickly as possible.
In this book, you will build an IMDB Top 250 clone, a content-based engine that works on
movie metadata, collaborative filters that make use of customer behavior data, and a hybrid
recommender that incorporates content-based and collaborative filtering techniques.
With this book, all you need to get started with building recommendation systems is
familiarity with Python, and by the time you're finished, you will have a great grasp of how
recommenders work, and you will be in a strong position to apply the techniques learned
to your own problem domains.