Book Image

Big Data Analytics with R and Hadoop

By : Vignesh Prajapati
Book Image

Big Data Analytics with R and Hadoop

By: Vignesh Prajapati

Overview of this book

<p>Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. New methods of working with big data, such as Hadoop and MapReduce, offer alternatives to traditional data warehousing. <br /><br />Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.<br /><br />You will start with the installation and configuration of R and Hadoop. Next, you will discover information on various practical data analytics examples with R and Hadoop. Finally, you will learn how to import/export from various data sources to R. Big Data Analytics with R and Hadoop will also give you an easy understanding of the R and Hadoop connectors RHIPE, RHadoop, and Hadoop streaming.</p>
Table of Contents (16 chapters)
Big Data Analytics with R and Hadoop
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Preface
Index

Recommendation algorithms


Recommendation is a machine-learning technique to predict what new items a user would like based on associations with the user's previous items. Recommendations are widely used in the field of e-commerce applications. Through this flexible data and behavior-driven algorithms, businesses can increase conversions by helping to ensure that relevant choices are automatically suggested to the right customers at the right time with cross-selling or up-selling.

For example, when a customer is looking for a Samsung Galaxy S IV/S4 mobile phone on Amazon, the store will also suggest other mobile phones similar to this one, presented in the Customers Who Bought This Item Also Bought window.

There are two different types of recommendations:

  • User-based recommendations: In this type, users (customers) similar to current user (customer) are determined. Based on this user similarity, their interested/used items can be recommended to other users. Let's learn it through an example.

    Assume...