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 Copyright Credits About the Authors About the Reviewer www.PacktPub.com Table of Contents Preface Chapter 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 Chapter 2: Data Mining Techniques Used in Recommender Systems Solving a data analysis problem Data preprocessing techniques Similarity measures Euclidian distance. Cosine distancePearson 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 Chapter 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 ratingExploring 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 dataData 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 Chapter 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 modelComparing models Identifying the most suitable model Optimizing a numeric parameter Summary Chapter 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 Appendix: References Index.

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.