Recommender systems are machine learning techniques that predict user purchases and preferences. There are several applications of recommender systems, such as online retailers and video-sharing websites.
This book teaches the reader how to build recommender systems using R. It starts by providing the reader with some relevant data mining and machine learning concepts. Then, it shows how to build and optimize recommender models using R and gives an overview of the most popular recommendation techniques. In the end, it shows a practical use case. After reading this book, you will know how to build a new recommender system on your own.
Chapter 1, Getting Started with Recommender Systems, describes the book and presents some real-life examples of recommendation engines.
Chapter 2, Data Mining Techniques Used in Recommender Systems, provides the reader with the toolbox to built recommender models: R basics, data processing, and machine learning techniques.
Chapter 3, Recommender Systems, presents some popular recommender systems and shows how to build some of them using R.
Chapter 4, Evaluating the Recommender Systems, shows how to measure the performance of a recommender and how to optimize it.
Chapter 5, Case Study – Building Your Own Recommendation Engine, shows how to solve a business challenge by building and optimizing a recommender.
You will need the R 3.0.0+, RStudio (not mandatory), and Samba 4.x Server software.
This book is intended for people who already have a background in R and machine learning. If you're interested in building recommendation techniques, this book is for you.
To cite the recommenderlab package (R package version 0.1-5) in publications, refer to recommenderlab: Lab for Developing and Testing Recommender Algorithms by Michael Hahsler at http://CRAN.R-project.org/package=recommenderlab
LaTeX users can use the following BibTeX entry:
@Manual{,
title = {recommenderlab: Lab for Developing and Testing
Recommender Algorithms},
author = {Michael Hahsler},
year = {2014},
note = {R package version 0.1-5},
url = { http://CRAN.R-
project.org/package=recommenderlab},
}In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We used the e1071 package to run SVM."
A block of code is set as follows:
vector_ratings <- factor(vector_ratings)qplot(vector_ratings) + ggtitle("Distribution of the ratings")
exten => i,1,Voicemail(s0)New terms and important words are shown in bold.
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