GraphAware Blog

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...

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.

Previous articleshave shown you how easy using Spring with Neo4j can be. Now the next release of Spring Data Neo4j (SDN), we are going to make this even easier!

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 maximizethe number of organizations and people you can reach. This means it is important to ensure your software is internationalized and localized.

Without question, Github is the biggest code sharing platform on the planet. With more than 14 millions users and 35 million repositories, the insights you can discover by analyzing the data available through its API are surprising and revealing.

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 the Bersin Predictions for 2016 report, Josh Bersin states that “it feels as though everything in the world of talent is changing – from the way we recruit and attract people, as well as how we reward them, to the way we learn, and how we curate and manage our entire work-life experience”[1].

A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way isstill a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amountof data and converting it into a useful source of knowledge for further processing.

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.

As of version 2.1, Neo4j OGM will support persistence events. Although a date for the release of 2.1 isn’t known at thetime of writing, we think this is an important and exciting new feature and so we’ll be writing a series of posts aboutit over the next few weeks to whet your appetites. In this first post we’ll take a quick tour of the new Events mechanismin the OGM, and provide some examples of how we might use it in our own applications. But first, some background…