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SkillsCast

Modelling a recommendation engine: A worked example

1st December 2015 in London at CodeNode

This SkillsCast was filmed at Modelling a recommendation engine: A worked example

In this session we'll build a recommendation engine from scratch while paying particular attention to the modelling choices made along the way.

Our solution will be a hybrid which makes uses of both content based and collaborative filtering and we'll be covering the following topics...

  • How do I know what nodes to create?
  • How should I be naming relationships?
  • Are my relationships too specific? Are they too generic?
  • How can I tell if I've got hidden nodes in my model?
  • How do I deal with time?
  • How do I evolve the model as new requirements come in?

...and any other modelling questions you have!

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Thanks to our sponsors

Modelling a recommendation engine: A worked example

Mark Needham

Mark is a graph advocate and field engineer for Neo Technology, the company behind the Neo4j graph database. As a field engineer, Mark helps customers embrace graph data and Neo4j by building sophisticated solutions to challenging data problems.

SkillsCast

In this session we'll build a recommendation engine from scratch while paying particular attention to the modelling choices made along the way.

Our solution will be a hybrid which makes uses of both content based and collaborative filtering and we'll be covering the following topics...

  • How do I know what nodes to create?
  • How should I be naming relationships?
  • Are my relationships too specific? Are they too generic?
  • How can I tell if I've got hidden nodes in my model?
  • How do I deal with time?
  • How do I evolve the model as new requirements come in?

...and any other modelling questions you have!

YOU MAY ALSO LIKE:

Thanks to our sponsors

About the Speaker

Modelling a recommendation engine: A worked example

Mark Needham

Mark is a graph advocate and field engineer for Neo Technology, the company behind the Neo4j graph database. As a field engineer, Mark helps customers embrace graph data and Neo4j by building sophisticated solutions to challenging data problems.