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.
Last month, I had the pleasure of speaking at GraphConnect in San Francisco, introducing the Graph-Aided Search to a large audience of Neo4j users and graph enthusiasts. For those who missed the conference, the recording and slides have now been made available. Enjoy and get in touch with feedback / questions!
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.