adam-cowley

CALL apoc.load.json('https://app.ticketmaster.com/discovery/v2/classifications?locale=en-us&apikey={key}') YIELD value AS json
UNWIND json._embedded.classifications as classification
MERGE (seg:Segment {ticketmaster_id: classification.segment.id, name: classification.segment.name})
WITH seg, classification
UNWIND classification.segment._embedded.genres as genre
MERGE (gen:Genre {ticketmaster_id: genre.id, name: genre.name})
MERGE (seg)-[:HAS_GENRE]->(gen)
WITH seg, gen, genre
UNWIND genre._embedded.subgenres as subgenre
MERGE (sub:SubGenre {ticketmaster_id: subgenre.id, name: subgenre.name})

adam-cowley

function queue(all) {
  return new Promise((resolve, reject) => {
    const output = [];

    next(all.splice(0, 1))
  
    function next(go) {
      go();
      .then(res => {
        output.push(res);

adam-cowley

== Northwind Graph

=== Introduction

Recently, I was asked to pitch a method for providing recommendations.  Luckily, armed with the knowledge obtained from talks from Max De Marzi and [Mark Needham](https://skillsmatter.com/skillscasts/7298-modelling-a-recommendation-engine-a-worked-example) at a recent  Neo4j London Meetups, I knew this could be easily achieved with Neo4j.

The key issue with recommendation engines comes from the data.  Luckily, Neo4j comes bundled with the Northwind Graph Example.  The Northwind database is an infamous dataset containing purchase history that has been used to teach relational databases for years and was a great place to start.  You can import the Northwind database into a graph by following the ["Import Data into Neo4j"](http://neo4j.com/developer/guide-importing-data-and-etl/) post on Neo4j or type the following into Neo4j's browser at `http://localhost:7474/`

    :play northwind graph