08 Mar 2017
by Miro Marchi
Recommendation engines are a crucial element in the global trend towards a push-based web experience and away from a pull-based one. They provide the ability to personalize content offered to each user by predicting the interest the user will have in the recommended items. This is not only a powerful business tool for content providers, but also a vital improvement to the user experience. In today’s world where the volume, interdependence, variety and speed of information is overwhelming, recommendation engines can significantly reduce the gap between us and what we search for. Indeed, these engines are used even to enhance common text based search (read more about graph-aided search in our blog).
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.