To get the most out of this book

This book will give you maximum benefit if you have some experience with Python development, or simply someone who wants to develop applications for social networking, news personalization, or smart advertising, this is the book for you. Having some knowledge of machine learning (ML) techniques will be helpful, but it is not mandatory.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Recommendation-Systems-with-Python. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/HandsOnRecommendationSystemswithPython_ColorImages.pdf.

Code in action

Visit the following link to check out videos of the code being run:

http://bit.ly/2JV4oeu.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Let's now implement the SVD filter using the surprise package."

A block of code is set as follows:

#Import SVD
from surprise import SVD

#Define the SVD algorithm object
svd = SVD()

#Evaluate the performance in terms of RMSE
evaluate(svd, data, measures=['RMSE'])

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

 else:
        #Default to a rating of 3.0 in the absence of any information
        wmean_rating = 3.0
    return wmean_rating
score(cf_user_wmean)

OUTPUT:
1.0174483808407588

Any command-line input or output is written as follows:

sudo pip3 install scikit-surprise

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "We see that the u.user file contains demographic information about our users, such as their age, sex, occupation, and zip_code."

Warnings or important notes appear like this.

Tips and tricks appear like this.