Frank Bedlam CalvinHarrisUK

## DatingGraph
= Dating =

Neo4j powers love. The online dating industry puts people’s interactions and connections at the very heart of its business. And these two concepts are closely linked. For example, if people have a connection to each other, through a shared friend or a shared interest, they are much more likely to get on. However, identifying these links is difficult – we all have a huge variety of interests and preferences that make us unique; making sense of all this data is a very complex task. This gist attempts to show how Neo4j has been used by clients to handle a such a significant task.

:neo4j-version: 2.0.0
:author: Amanda
:twitter: @pandamonial
:tags: domain:Dating


## RetailGraph
= Retail Graph =

The significant functional differentiation of Neo4j in the retail space is the ability to quickly traverse interconnected datasets and make recommendations in “real time”.  Competing approaches require large batch processing activities done offline to compute recommendations against large aggregate datasets, usually as nightly routines.  Others attempt to parameterize or profile products and users, leaving the connection of interests and products simply to keyword matching and categorization. These methods produce results that can quickly become stale and outdated if they are not done frequently and lack flexibility and capabilities to dynamically change .

:neo4j-version: 2.0.1
:author: Amanda
:twitter: @pandamonial
:tags: domain:Retail


## LearningStyleGraph
= Personalized Learning =

Personalized learning is a problem that has yet to be solved in the real world. Students learn in all sorts of different ways and each student could achieve more if concepts are presented to them in a way that matches their personal learning style. It's not possible to have a teacher assigned to every individual student but if we use automation and graph theory, we can start to get closer to just that. In this Gist I will attempt to show that by tracking specific information about learning modules and student's testing achievements, we can present concepts to a student in a way that can help them learn more effectively.

:neo4j-version: 2.0.0
:author: Amanda
:twitter: @pandamonial
:tags: domain:Education

The domain model:
	= Dating =

	Neo4j powers love. The online dating industry puts people’s interactions and connections at the very heart of its business. And these two concepts are closely linked. For example, if people have a connection to each other, through a shared friend or a shared interest, they are much more likely to get on. However, identifying these links is difficult – we all have a huge variety of interests and preferences that make us unique; making sense of all this data is a very complex task. This gist attempts to show how Neo4j has been used by clients to handle a such a significant task.

	:neo4j-version: 2.0.0
	:author: Amanda
	:twitter: @pandamonial
	:tags: domain:Dating
	= Retail Graph =

	The significant functional differentiation of Neo4j in the retail space is the ability to quickly traverse interconnected datasets and make recommendations in “real time”. Competing approaches require large batch processing activities done offline to compute recommendations against large aggregate datasets, usually as nightly routines. Others attempt to parameterize or profile products and users, leaving the connection of interests and products simply to keyword matching and categorization. These methods produce results that can quickly become stale and outdated if they are not done frequently and lack flexibility and capabilities to dynamically change .

	:neo4j-version: 2.0.1
	:author: Amanda
	:twitter: @pandamonial
	:tags: domain:Retail
	= Personalized Learning =

	Personalized learning is a problem that has yet to be solved in the real world. Students learn in all sorts of different ways and each student could achieve more if concepts are presented to them in a way that matches their personal learning style. It's not possible to have a teacher assigned to every individual student but if we use automation and graph theory, we can start to get closer to just that. In this Gist I will attempt to show that by tracking specific information about learning modules and student's testing achievements, we can present concepts to a student in a way that can help them learn more effectively.

	:neo4j-version: 2.0.0
	:author: Amanda
	:twitter: @pandamonial
	:tags: domain:Education

	The domain model: