Building a Recommendation Engine with Scala（Paperback）

Learn to use Scala to build a recommendation engine from scratch and empower your website users

About This Book

• Learn the basics of a recommendation engine and its application in e-commerce
• Discover the tools and machine learning methods required to build a recommendation engine
• Explore different kinds of recommendation engines using Scala libraries such as MLib and Spark

Who This Book Is For

This book is written for those who want to learn the different tools in the Scala ecosystem to build a recommendation engine. No prior knowledge of Scala or recommendation engines is assumed.

What You Will Learn

• Discover the tools in the Scala ecosystem
• Understand the challenges faced in e-commerce systems and learn how you can solve those challenges with a recommendation engine
• Familiarise yourself with machine learning algorithms provided by the Apache Spark framework
• Build different versions of recommendation engines from practical code examples
• Enhance the user experience by learning from user feedback
• Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations

In Detail

With an increase of data in online e-commerce systems, the challenges in assisting users with narrowing down their search have grown dramatically. The various tools available in the Scala ecosystem enable developers to build a processing pipeline to meet those challenges and create a recommendation system to accelerate business growth and leverage brand advocacy for your clients.

This book provides you with the Scala knowledge you need to build a recommendation engine.

You'll be introduced to Scala and other related tools to set the stage for the project and familiarise yourself with the different stages in the data processing pipeline, including at which stages you can leverage the power of Scala and related tools. You'll also discover different machine learning algorithms using MLLib.

As the book progresses, you will gain detailed knowledge of what constitutes a collaborative filtering based recommendation and explore different methods to improve users' recommendation.