Building a Recommendation System with R : Learn the Art of Building Robust and Powerful Recommendation Engines Using R

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. Cover 1 Copyright 3 Credits 4 About the Authors 5 About the Reviewer 6 www.PacktPub.com 7 Table of Contents 12 Preface 16 Chapter 1: Getting Started with Recommender Systems 20 Understanding recommender systems 20 The structure of the book 21 Collaborative filtering recommender systems 22 Content-based recommender systems 22 Knowledge-based recommender systems 23 Hybrid systems 24 Evaluation techniques 24 A case study 25 The future scope 25 Summary 25 Chapter 2: Data Mining Techniques Used in Recommender Systems 26 Solving a data analysis problem 27 Data preprocessing techniques 28 Similarity measures 28 Euclidian distance 28 Cosine distance 29 Pearson correlation 29 Dimensionality reduction 30 Principal component analysis 30 Data mining techniques 34 Cluster analysis 34 Explaining the k-means cluster algorithm 35 Support vector machine 37 Decision trees 40 Ensemble methods 42 Bagging 42 Random forests 43 Boosting 44 Evaluating data-mining algorithms 46 Summary 49 Chapter 3: Recommender Systems 50 R package for recommendation – recommenderlab 50 Datasets 51 Jester5k, MSWeb, and MovieLense 51 The class for rating matrices 52 Computing the similarity matrix 53 Recommendation models 55 Data exploration 57 Exploring the nature of the data 57 Exploring the values of the rating 58 Exploring which movies have been viewed 59 Exploring the average ratings 60 Visualizing the matrix 62 Data preparation 66 Selecting the most relevant data 66 Exploring the most relevant data 67 Normalizing the data 68 Binarizing the data 70 Item-based collaborative filtering 72 Defining the training and test sets 73 Building the recommendation model 74 Exploring the recommender model 76 Applying the recommender model on the test set 79 User-based collaborative filtering 83 Building the recommendation model 84 Applying the recommender model on the test set 85 Collaborative filtering on binary data 87 Data preparation 88 Item-based collaborative filtering on binary data 89 User-based collaborative filtering on binary data 91 Conclusions about collaborative filtering 92 Limitations of collaborative filtering 92 Content-based filtering 93 Hybrid recommender systems 93 Knowledge-based recommender systems 94 Summary 94 Chapter 4: Evaluating the Recommender Systems 96 Preparing the data to evaluate the models 96 Splitting the data 97 Bootstrapping data 100 Using k-fold to validate models 102 Evaluating recommender techniques 103 Evaluating the ratings 103 Evaluating the recommendations 107 Identifying the most suitable model 110 Comparing models 111 Identifying the most suitable model 113 Optimizing a numeric parameter 114 Summary 116 Chapter 5: Case Study – Building Your Own Recommendation Engine 118 Preparing the data 119 Description of the data 119 Importing the data 119 Defining a rating matrix 121 Extracting item attributes 127 Building the model 129 Evaluating and optimizing the model 138 Building a function to evaluate the model 138 Optimizing the model parameters 141 Summary 148 Appendix: References 150 Index 152

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.

Learn the art of building robust and powerful recommendation engines using RKey FeaturesBook DescriptionWhat you will learnGet to grips with the most important branches of recommendationUnderstand various data processing and data mining techniquesEvaluate and optimize the recommendation algorithmsPrepare and structure the data before building modelsDiscover different recommender systems along with their implementation in RExplore various evaluation techniques used in recommender systemsGet to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systemsWho this book is forIf 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.