Just like the knowledge-based and content-based recommenders, we will build our collaborative filtering models in the context of movies. Since collaborative filtering demands data on user behavior, we will be using a different dataset known as MovieLens.
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Book Overview & Buying
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Table Of Contents
Hands-On Recommendation Systems with Python
By :
Hands-On Recommendation Systems with Python
By:
Overview of this book
Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix 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 your platform.
This book shows you how to do just that. You will learn about the 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 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. You'll use collaborative filters to 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 a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.
Table of Contents (9 chapters)
Preface
Free Chapter
Getting Started with Recommender Systems
Manipulating Data with the Pandas Library
Building an IMDB Top 250 Clone with Pandas
Building Content-Based Recommenders
Getting Started with Data Mining Techniques
Building Collaborative Filters
Hybrid Recommenders
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