Book Image

Hands-On Recommendation Systems with Python

By : Rounak Banik
Book Image

Hands-On Recommendation Systems with Python

By: Rounak 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)

Exporting the clean DataFrame

In the previous chapter, we performed a series of data wrangling and cleaning processes on our metadata in order to convert it into a form that was more usable. To avoid having to perform these steps again, let's save this cleaned DataFrame into a CSV file. As always, doing this with pandas happens to be extremely easy.

In the knowledge recommender notebook from Chapter 4, enter the following code in the last cell:

#Convert the cleaned (non-exploded) dataframe df into a CSV file and save it in the data folder
#Set parameter index to False as the index of the DataFrame has no inherent meaning.
df.to_csv('../data/metadata_clean.csv', index=False)

Your data folder should now contain a new file, metadata_clean.csv.

Let's create a new folder, Chapter 4, and open a new Jupyter Notebook within this folder. Let's now import our new...