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Hands-On Recommendation Systems with Python

Hands-On Recommendation Systems with Python

By : Banik
3.4 (11)
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Hands-On Recommendation Systems with Python

Hands-On Recommendation Systems with Python

3.4 (11)
By: Banik

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)
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Manipulating Data with the Pandas Library

In the next few portions of the book, we are going to get our hands dirty by building the various kinds of recommender systems that were introduced in chapter one. However, before we do so, it is important that we know how to handle, manipulate, and analyze data efficiently in Python.

The datasets we'll be working with will be several megabytes in size. Historically, Python has never been well-known for its speed of execution. Therefore, analyzing such huge amounts of data using vanilla Python and the built-in data structures it provides us is simply impossible.

In this chapter, we're going to get ourselves acquainted with the pandas library, which aims to overcome the aforementioned limitations, making data analysis in Python extremely efficient and user-friendly. We'll also introduce ourselves to the Movies Dataset that...

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