Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (23 chapters)
Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
16
Sources

An overview of a recommendation engine


We will now focus on situations where users have provided rankings or ratings on previously viewed or purchased items. There are two primary categories of designing recommendation systems: collaborative filtering and content-based (Ansari, Essegaier, and Kohli, 2000). The former category is what we will concentrate on, as this is the focus of the recommenderlab R package that we will be using.

For content-based approaches, the concept is to link user preferences with item attributes. These attributes may be things such as the genre, cast, or storyline for a movie or TV show recommendation. As such, recommendations are based entirely on what the user provides as ratings; there is no linkage to what anyone else recommends. This has the advantage over content-based approaches in that when a new item is added, it can be recommended to a user if it matches their profile, instead of relying on other users to rate it first (the so-called "first rater problem...