Applied Recommender Systems with Python

This book is dedicated to data scientists who are starting new recommendation engine projects from scratch but don’t have prior experience in this domain. They can easily learn concepts and gain practical knowledge with this book. Recommendation engines
have recently gained a lot of traction and popularity in different domains and have a proven track record for increasing sales and revenue.
This book is divided into eleven chapters. The first section, Chapters 1 and 2, covers basic approaches. The following section, which consists of Chapters 3, 4, 5, and 6, covers popular methods, including collaborative filtering-based, content-based, and hybrid
recommendation systems. The next section, Chapters 7 and 8, discusses implementing systems using state-of-the-art machine learning algorithms. Chapters 9, 10, and 11 discuss trending and emerging techniques in recommendation systems.
The code for the implementations in each chapter and the required datasets are available on GitHub at github.com/apress/applied-recommender-systems-python.
To successfully perform all the projects in this book, you need Python 3.x or higher running on any Windows- or Unix-based operating system with a processor of 2.0 GHz or higher and a minimum of 4 GB RAM. You can download Python from Anaconda and
leverage a Jupyter notebook for all coding purposes. This book assumes you know Keras basics and how to install machine learning and deep learning basic libraries. Please upgrade or install the latest versions of all the libraries.