Recommender systems are machine learning techniques that predict user purchases and preferences. There are several applications of recommender systems, such as online retailers and video-sharing websites.
This book teaches the reader how to build recommender systems using R. It starts by providing the reader with some relevant data mining and machine learning concepts. Then, it shows how to build and optimize recommender models using R and gives an overview of the most popular recommendation techniques. In the end, it shows a practical use case. After reading this book, you will know how to build a new recommender system on your own.
Chapter 1, Getting Started with Recommender Systems, describes the book and presents some real-life examples of recommendation engines.
Chapter 2, Data Mining Techniques Used in Recommender Systems, provides the reader with the toolbox to built recommender models: R basics, data processing, and machine learning techniques.
Chapter 3, Recommender Systems, presents some popular recommender systems and shows how to build some of them using R.
Chapter 4, Evaluating the Recommender Systems, shows how to measure the performance of a recommender and how to optimize it.
Chapter 5, Case Study – Building Your Own Recommendation Engine, shows how to solve a business challenge by building and optimizing a recommender.