20 Apr 2016
by Alessandro Negro and Christophe Willemsen
In our previous blog post
we introduced the concept of Graph Aided Search. It refers to a personalised user experience during search where the
results are customised for each user based on information gathered about them (likes, friends, clicks, buying history, etc.).
This information is stored in a graph database and processed using machine learning and/or graph analysis algorithms.
20 Oct 2015
by Alessandro Negro & Christophe Willemsen
Recently, Neo Technology announced the 2.3.0-RC1 release of their Neo4j graph database. One of the key new features is Triadic
Selection built into Cypher’s Cost Based Planner. In this blog post, we will explore the Triadic Selection in detail
and demonstrate how significantly it can speed up recommendations computed in Neo4j.
30 Sep 2015
by Michal Bachman
For the last couple of years, Neo4j has been increasingly popular as the technology of choice for people building
real-time recommendation engines. Having been at the forefront of the graph movement through client
engagements and open-source software development, we have identified the next step in the natural evolution of graph-based recommendation
engines. We call it Graph-Aided Search.