Free Building A Recommendation System With R Ebooks Online

                
              
Free 
Building A Recommendation System With R Ebooks Online
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.
File Size: 3564 KB
Print Length: 158 pages
Publisher: Packt Publishing; 1 edition (September 29, 2015)
Publication Date: September 29, 2015
Sold by:  Digital Services LLC
Language: English
ASIN: B012O8S1YM
Text-to-Speech:  Enabled
X-Ray:     Not Enabled
Word Wise: Not Enabled
Lending: Not Enabled
Enhanced Typesetting:  Not Enabled
Best Sellers Rank: #880,885 Paid in Kindle Store (See Top 100 Paid in Kindle Store)   #226 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Machine Theory   #944 in Books > Computers & Technology > Software > Mathematical & Statistical   #1107 in Books > Computers & Technology > Databases & Big Data > Data Processing
This is, as far as I know, the only book on the subject of recommender systems in R. The book is a great resource for those interested in building a recommender system in R from the grounds up. The authors start by giving a good overview of the recommender problems with detailed examples, then in the second chapter they cover the techniques used in recommender systems. The last three chapters present the in-depth process of building and evaluating a recommender system in R. Chapter 5 gives a very detailed example of building a recommender system using unstructured data. I would highly recommend this book!
As a software engineer and data scientist, I read a lot of technical books. This one is a poorly executed attempt at explaining how to build recommendation systems in R. The book suffers from syntax errors and poor editing - downloading the dataset required an email because of problems with their [the publisher's] website. The kindle version (at least on kindle cloud reader) does not allow copy/paste -- must manually type to do exercises. This is the last publication I will buy from Packt Publishers, and I will do my best to persuade others to boycott this company. If you want to buy a book on recommendation engines, I would go with "Recommender Systems" by Charu Aggarwal (Springer Press).
The book provides a good account of a single case study, but not much more. The first couple of chapters are short, general description of few well rehearsed machine-leaning algorithms. However, if you really want to understand those algorithms you will have to look elsewhere for more elaborate sources, and better sources are abound. As for the recommendation system case study, it is quite detailed and can possibly be adapted to other applications. So if this is the one thing you are after, the book fits.
This book provides a good and quick introduction to recommender systems (RS) and implementation details of such systems in R. It starts from basic definitions and an overview of different types of RS, followed by a non-mathematical description of several, most common data mining algorithms that could be used in RS. The R package 'recommenderlab' is discussed in detail, together with numerous R snippets demonstrating the package functionality on the MovieLense database of real recommendations. The important task of RS performance evaluation is given enough space as well as the whole chapter of how to build your own recommendation package based on recommenderlab functions is provided.Thus, entrants into this field who have basic knowledge of data mining algorithms as well as R may find this book easy to read and understand. However, for more experience users, especially with RS experience, this book might not bring much new information: The important question of how RS perform recommendations when either a product is totally new (there are still no users who reviewed it) or a user doesn't yet have any reviews is only briefly touched.
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