Building Recommendation Engines

Building Recommendation Engines

Building Recommendation Engines by Sureshkumar Gorakala
English | 5 Jan. 2017 | ISBN: 1785884859 | 357 Pages | AZW3/MOBI/EPUB/PDF (conv) | 81.07 MB

Key Features

A step-by-step guide to building recommendation engines that are personalized, scalable, and real time
Get to grips with the best tool available on the market to create recommender systems
This hands-on guide shows you how to implement different tools for recommendation engines, and when to use which

Book Description

A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general.

The book starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation to ensure each pick is the one that suits you the best.

During the course of the book, you will create simple recommendation engine, real-time recommendation engine, scalable recommendation engine, and more. You will familiarize yourselves with various techniques of recommender systems such as collaborative, content-based, and cross-recommendations before getting to know the best practices of building a recommender system towards the end of the book!

What you will learn

Build your first recommendation engine
Discover the tools needed to build recommendation engines
Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations
Create efficient decision-making systems that will ease your work
Familiarize yourself with machine learning algorithms in different frameworks
Master different versions of recommendation engines from practical code examples
Explore various recommender systems and implement them in popular techniques with R, Python, Spark, and others

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