Building a Recommendation System with R

Suresh K. Gorakala, Michele Usuelli

• Publisher: Packt Publishing
• Year: 2015
• eBook ISBN: 9781783554492
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

Learn the art of building robust and powerful recommendation engines using R

About This Book

• Learn to exploit various data mining techniques
• Understand some of the most popular recommendation techniques
• This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines

Who This Book Is For

If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you.

What You Will Learn

• Get to grips with the most important branches of recommendation
• Understand various data processing and data mining techniques
• Evaluate and optimize the recommendation algorithms
• Prepare and structure the data before building models
• Discover different recommender systems along with their implementation in R
• Explore various evaluation techniques used in recommender systems
• Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems

In Detail

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.

Style and approach

This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

Building a Recommendation System with R

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Table of Contents

Building a Recommendation System with R

Credits

About the Authors

About the Reviewer

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Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Citation

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

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

Solving a data analysis problem

Data preprocessing techniques

Similarity measures

Euclidian distance

Cosine distance

Pearson correlation

Dimensionality reduction

Principal component analysis

Data mining techniques

Cluster analysis

Explaining the k-means cluster algorithm

Support vector machine

Decision trees

Ensemble methods

Bagging

Random forests

Boosting

Evaluating data-mining algorithms

Summary

3. Recommender Systems

R package for recommendation – recommenderlab

Datasets

Jester5k, MSWeb, and MovieLense

The class for rating matrices

Computing the similarity matrix

Recommendation models

Data exploration

Exploring the nature of the data

Exploring the values of the rating

Exploring which movies have been viewed

Exploring the average ratings

Visualizing the matrix

Data preparation

Selecting the most relevant data

Exploring the most relevant data

Normalizing the data

Binarizing the data

Item-based collaborative filtering

Defining the training and test sets

Building the recommendation model

Exploring the recommender model

Applying the recommender model on the test set

User-based collaborative filtering

Building the recommendation model

Applying the recommender model on the test set

Collaborative filtering on binary data

Data preparation

Item-based collaborative filtering on binary data

User-based collaborative filtering on binary data

Conclusions about collaborative filtering

Limitations of collaborative filtering

Content-based filtering

Hybrid recommender systems

Knowledge-based recommender systems

Summary

4. Evaluating the Recommender Systems

Preparing the data to evaluate the models

Splitting the data

Bootstrapping data

Using k-fold to validate models

Evaluating recommender techniques

Evaluating the ratings

Evaluating the recommendations

Identifying the most suitable model

Comparing models

Identifying the most suitable model

Optimizing a numeric parameter

Summary

5. Case Study – Building Your Own Recommendation Engine

Preparing the data

Description of the data

Importing the data

Defining a rating matrix

Extracting item attributes

Building the model

Evaluating and optimizing the model

Building a function to evaluate the model

Optimizing the model parameters

Summary

A. References

Index

Building a Recommendation System with R

Copyright © 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

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Table of contents

• Building a Recommendation System with R