Book Image

Learning Neo4j 3.x - Second Edition

By : Jerome Baton
Book Image

Learning Neo4j 3.x - Second Edition

By: Jerome Baton

Overview of this book

Neo4j is a graph database that allows traversing huge amounts of data with ease. This book aims at quickly getting you started with the popular graph database Neo4j. Starting with a brief introduction to graph theory, this book will show you the advantages of using graph databases along with data modeling techniques for graph databases. You'll gain practical hands-on experience with commonly used and lesser known features for updating graph store with Neo4j's Cypher query language. Furthermore, you'll also learn to create awesome procedures using APOC and extend Neo4j's functionality, enabling integration, algorithmic analysis, and other advanced spatial operation capabilities on data. Through the course of the book you will come across implementation examples on the latest updates in Neo4j, such as in-graph indexes, scaling, performance improvements, visualization, data refactoring techniques, security enhancements, and much more. By the end of the book, you'll have gained the skills to design and implement modern spatial applications, from graphing data to unraveling business capabilities with the help of real-world use cases.
Table of Contents (24 chapters)
Title Page
Credits
About the Authors
Acknowledgement
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Business variations on recommendations


The entire principle of a recommender system, as we described before, can be generalized into a different kind of system that has many other business applications. Some people would call it a rules engine, which does some kind of sophisticated if-this-then-that matching and figures out what action to take at the other end of the decision tree. Other people may call it a pattern-matching system, which could be applied to any kind of pattern and tied to any kind of action. Most likely, graph databases such as Neo4j hold some characteristics of all of the above and provide you with an interesting infrastructural optimization that could serve well.

Before wrapping up this chapter, we would like to highlight some use cases that are extremely related to the recommender system use case. Let's go through some well-known sweet spot applications that essentially use the same principles underneath.