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Building a Recommendation System with R

Building a Recommendation System with R

By : Usuelli
4.5 (8)
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Building a Recommendation System with R

Building a Recommendation System with R

4.5 (8)
By: Usuelli

Overview of this book

A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system.
Table of Contents (8 chapters)
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6
A. References
7
Index

R package for recommendation – recommenderlab


In this chapter, we will build recommender systems using recommenderlab, which is an R package for collaborative filtering. This section will present a quick overview of this package. First, let's install it, if we haven't done so already:

if(!"recommenderlab" %in% rownames(installed.packages())){install.packages("recommenderlab")}

Now, we can load the package. Then, using the help function, we can take a look at its documentation:

library("recommenderlab")
help(package = "recommenderlab")

When we run the preceding command in RStudio, a help file containing some links and a list of functions will open.

The examples that you will see in this chapter contain some random components. In order to be able to reproduce the code obtaining the same output, we need to run this line:

set.seed(1)

We are now ready to start exploring recommenderlab.

Datasets

Like many other R packages, recommenderlab contains some datasets that can be used to play around with the functions...

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