12/28/2023 0 Comments Neo4j series![]() We use dynamic queueing so that the people who we really want at the festival can buy tickets. Take an example in which a festival is selling 200,000 tickets, but 500,000 people want to buy tickets. We use all of this data to create a dynamic queuing score, which allows us to sell tickets to high-spending fans and top influencers to maximize revenue. – all of which are things that are sent back to our database in real time. This includes data related to ticket sales, Facebook views, interactions and how many friends they have on Facebook to determine the size of their influence, etc. What we’re going to be talking about today is how we used Neo4j to provide real-time recommendations in the context of events and ticket sales, both on the front-end for customers and on the back-end for ticket queueing: The fast, scalable and highly-performant Neo4j graph database provided Prepr with exactly what they needed to create these highly effective tools, while requiring less storage and hardware than their previous database.įull Presentation: Large-Scale Real-Time Recommendations with Neo4j On the customer-facing side, Prepr was able to provide users of the website I amsterdam, one of the largest tourism websites in the city, with a chatbot that provided real-time event recommendations based on things like the user’s location, the time of day and the weather. ![]() These recommendations were based on the real-time analysis of information such as the number of social media followers of a customer, or the amount of money they spent at the festival in prior years. For one of the largest events companies in the Netherlands, Prepr created a dynamic queueing tool that could identify customers in a queue that would be most beneficial for that company to sell tickets to. They were using the MySQL relational database to manage their data, but it was becoming increasingly slow - and they had millions of data points and properties to manage.īy switching from MySQL to Neo4j, they were able to provide powerful, real-time recommendations on both the back and front-end of the user experience. ![]() Prepr is a multi-channel engagement platform that streamlines content workflows and powers valuable audience interactions. Editor’s Note: This presentation was given by Tim Hanssen at GraphConnect 2018 in New York City.
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