![]() You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. No need to wade through tons of machine learning theory-you'll get started with building and learning about recommenders as quickly as possible. ![]() You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. Recommendation systems are at the heart of almost every internet business today from Facebook to Netflix to Amazon.
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