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Building a Recommendation System with R

By : Usuelli

4.5 (8)

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Citation

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with Recommender Systems

1. Getting Started with Recommender Systems

Understanding recommender systems

The structure of the book

Collaborative filtering recommender systems

Content-based recommender systems

Knowledge-based recommender systems

Hybrid systems

Evaluation techniques

A case study

The future scope

Summary

2. Data Mining Techniques Used in Recommender Systems

2. Data Mining Techniques Used in Recommender Systems

Solving a data analysis problem

Data preprocessing techniques

Data mining techniques

Cluster analysis

Decision trees

Ensemble methods

Evaluating data-mining algorithms

Summary

3. Recommender Systems

3. Recommender Systems

R package for recommendation – recommenderlab

Data exploration

Data preparation

Item-based collaborative filtering

User-based collaborative filtering

User-based collaborative filtering on binary data

Content-based filtering

Hybrid recommender systems

Knowledge-based recommender systems

Summary

4. Evaluating the Recommender Systems

4. Evaluating the Recommender Systems

Preparing the data to evaluate the models

Evaluating recommender techniques

Identifying the most suitable model

Summary

5. Case Study – Building Your Own Recommendation Engine

5. Case Study – Building Your Own Recommendation Engine

Preparing the data

Building the model

Evaluating and optimizing the model

Summary

A. References

A. References

Index

Index

Data exploration

In this section, we will explore the MovieLense dataset. For this purpose, we will use recommenderlab to build recommender systems and ggplot2 to visualize their results. Let's load the packages and the data:

library("recommenderlab")
library("ggplot2")
data(MovieLense)
class(MovieLense)
## [1] "realRatingMatrix"
## attr(,"package")
## [1] "recommenderlab"

MovieLense is a realRatingMatrix object containing a dataset about movie ratings. Each row corresponds to a user, each column to a movie, and each value to a rating.

Exploring the nature of the data

Let's take a quick look at MovieLense. As explained in the previous section, there are some generic methods that can be applied to realRatingMatrix objects. We can extract their size using dim:

dim(MovieLense)
## [1]  943 1664

There are 943 users and 1664 movies. Since realRatingMatrix is an S4 class, the components of the objects are contained in MovieLense slots. We can...

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Tech Concepts

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Programming languages

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