Over 10,000 physical typewritten documents from 1932 to 1941 had to be digitised, structured, and connected in order to create a single, centralised source of knowledge, for enabling the analysis of historical processes.
Luanne Misquitta explains how to produce good starting-point recommendations for whisky using Cypher that are of higher quality than those we see at our favourite online stores.
From user preferences and location to time of day and weather, complex context representations have been the key to delivering personalised content. Graph databases excel at dealing with large amounts of complex data and therefore, they have been at the core of many modern real-time recommendation systems. In the near future, graph databases will play an equally important role in search personalisation.
Graph Databases are naturally well-suited for building recommendation engines. In this talk, Christophe will share his experience building a number of production-ready recommendation engines using Neo4j and introduce the open-source GraphAware Reco4PHP Library, which enables PHP developers to rapidly build their own recommendation systems.
‘GraphAware successfully implemented a real-time recommendation engine on our site in just two weeks. That is an astonishingly short time to production.’
--David Stephenson, Managing Director, DSI Analytics and interim Head of Analytics, Belvilla
See a case study how GraphAware helped its client, InfoJobs develop its contact recommendation system and improve development skills for life.
‘GraphAware didn’t just help us build our recommendation service: they helped our developers acquire a whole new set of programming skills.’
--Marc Pou, Product Owner, InfoJobs
Real-Time Recommendations with Graphs and the Future of Search: Michal Bachman, Managing Director, GraphAware. Michal talks about how they use Neo4j in combination with Elasticsearch to power real-time recommendations.