Find out what's new in the Neo4j world
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.
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.
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.
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”.
A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way is still a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amount of data and converting it into a useful source of knowledge for further processing.
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.
At GraphAware, we live and breathe Neo4j. For three years, we have been helping customers around the world embrace this amazing technology as a solution to many interesting problems. Mainstream applications of graphs, such as real-time recommendations, fraud detection, impact analysis, and graph-aided search, have been getting a lot of media attention.