“Relevance is the practice of improving search results for users by satisfying their information needs in the context of a particular user experience, while balancing how ranking impacts business’s needs.” 
During GraphConnect San Francisco 2015, we introduced the concept of Graph-Aided Search and released the first module providing Neo4j data replication to Elasticsearch.
In our previous blog postwe introduced the concept of Graph Aided Search. It refers to a personalised user experience during search where theresults 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.
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 clientengagements and open-source software development, we have identified the next step in the natural evolution of graph-based recommendationengines. We call it Graph-Aided Search.