Building a Recommendation System with R

Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 29. September 2015
  • |
  • 158 Seiten
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-78355-450-8 (ISBN)
Learn the art of building robust and powerful recommendation engines using RAbout This BookLearn to exploit various data mining techniquesUnderstand some of the most popular recommendation techniquesThis is a step-by-step guide full of real-world examples to help you build and optimize recommendation enginesWho 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.What 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 systemsIn DetailA 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 approachThis 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.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
978-1-78355-450-8 (9781783554508)
1783554509 (1783554509)
weitere Ausgaben werden ermittelt
Suresh K. Gorakala is a blogger, data analyst, and consultant on data mining, big data analytics, and visualization tools. Since 2013, he has been writing and maintaining a blog on data science at
Suresh holds a bachelor's degree in mechanical engineering from SRKR Engineering College, which is affiliated with Andhra University, India.
He loves generating ideas, building data products, teaching, photography, and travelling. Suresh can be reached at can also follow him on Twitter at @sureshgorakala. Michele Usuelli is a data scientist, writer, and R enthusiast specialized in the fields of big data and machine learning. He currently works for Revolution Analytics, the leading R-based company that got acquired by Microsoft in April 2015. Michele graduated in mathematical engineering and has worked with a big data start-up and a big publishing company in the past. He is also the author of R Machine Learning Essentials, Packt Publishing.
  • Cover
  • Copyright
  • Credits
  • About the Authors
  • About the Reviewer
  • 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 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
  • 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 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
  • 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 model
  • Comparing 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

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