Speaker "William Lyon" Details Back



Building a Real Time Recommender System with Graphs, Neo4j and Python


In this session we will show how to build a recommendation engine using Neo4j and Python. Our solution will be a hybrid which makes uses of both content based and collaborative filtering to come up with multi layered recommendations that take different datasets into account e.g. we'll combine data from the and twitter APIs. We'll evolve the solution from scratch and look at the decisions we make along the way in terms of modeling and coming up with factors that might lead to better recommendations for the end user.


William Lyon is a software developer at Neo4j, the open source graph database. As an engineer on the Developer Relations team, he works primarily on integrating Neo4j with other technologies, building demo apps, helping other developers build applications with Neo4j, and writing documentation. Prior to joining Neo, William worked as a software developer for several startups in the real estate software, quantitative finance, and predictive API fields. William holds a Masters degree in Computer Science from the University of Montana. You can find him online at