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 kwledge 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 kw 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.
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 http://www.dataperspective.info/. 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 firstname.lastname@example.org.You 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.