GraphAware Blog

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GraphAware at GraphConnect San Francisco 2016

25 Nov 2016 by Miro Marchi Neo4j Conference GraphAware

Last month, the 5th edition of GraphConnect San Francisco took place at the Hyatt Regency SF. It was the biggest graph technology event ever and GraphAware proudly contributed as a sponsor, with one main talk, two lightning talks and our GraphHero stand 。^‿^。 This edition’s big announcement was the upcoming new landmark release of Neo4j 3.1, “The database for the connected enterprise”, which introduces a new state-of-the-art clustering architecture and new security architecture to meet enterprise requirements for scale and security. There will be a lot to say about this release, but you can already try the beta release as we have done!

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Using NLP + Neo4j for a Social Media Recommendation Engine

04 Oct 2016 by Alessandro Negro Neo4j NLP

In recent years, the rapid growth of social media communities has created a vast amount of digital documents on the web. Recommending relevant documents to users is a strategic goal for the effectiveness of customer engagement but at the same time is not a trivial problem.

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Internationalization with CypherMessageSource, Spring and Neo4j

29 Sep 2016 by Eric Spiegelberg, Guest Author Neo4j SDN Internationalization Localization

Whether you realize it or not, the software you create has a global market. Perhaps more so than any other product in any other industry, code that may start as a small, individual effort has the potential to rapidly blossom into a product used around the world. While it is not always obvious that your application can or will have such wide usage, it is in your best interest to maximize the number of organizations and people you can reach. This means it is important to ensure your software is internationalized and localized.

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Graph-Aided Search - The Rise of Personalised Content

20 Apr 2016 by Alessandro Negro and Christophe Willemsen Neo4j Cypher Recommendations Elasticsearch

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.

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