Recommendation systems are at the heart of almost every internet business today, from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use and buy from your platform.
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.
