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Learning Neo4j

You're reading from   Learning Neo4j Run blazingly fast queries on complex graph datasets with the power of the Neo4j graph database

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Product type Paperback
Published in Aug 2014
Publisher
ISBN-13 9781849517164
Length 222 pages
Edition 1st Edition
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Author (1):
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Rik Van Bruggen Rik Van Bruggen
Author Profile Icon Rik Van Bruggen
Rik Van Bruggen
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Table of Contents (13) Chapters Close

Preface 1. Graphs and Graph Theory – an Introduction FREE CHAPTER 2. Graph Databases – Overview 3. Getting Started with Neo4j 4. Modeling Data for Neo4j 5. Importing Data into Neo4j 6. Use Case Example – Recommendations 7. Use Case Example – Impact Analysis and Simulation 8. Visualizations for Neo4j 9. Other Tools Related to Neo4j A. Where to Find More Information Related to Neo4j B. Getting Started with Cypher Index

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.

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