With today’s blog, we will dive a little bit into front-end application development. When creating complex applications, as we do in GraphAware, we need to handle large and complicated application states to present our users with correct user interfaces. Making application state predictable and explainable is one of the key challenges for the successful agile frontend development team. As we often like to point out, graphs are proving to be a successful solution to many existing problems. So, could we learn from graphs to handle application state in a more predictable manner?
Graph-Powered Machine Learning has already introduced us to content-based recommendations and collaborative filtering. These are the two most used approaches to providing recommendations. However, they both need information about the users to do so. What if you do not have user information? That’s where session-based recommendations come in.
We are proud to announce the 2.13 release of Hume. Advanced Expand has been extended to include much more functionality, so you build a large variety of queries visually. We also boosted the capabilities of Alerts.
Welcome back to the Graph-Powered Machine Learning book club. Now we are in the section of the book that focuses on recommendations. In the last blog, I summed up how content-based recommendations work. In the fifth chapter, the author Alessandro Negro introduces us to collaborative filtering.
Organisations face a dilemma: the data they have gathered bears immense potential, yet leveraging it is a significant challenge. This blog post outlines an approach to solving this problem by combining graph technology with data orchestration.
A couple of days ago, we hosted a Logistics Optimisation webinar. We covered some challenges of Logistics Chains and talked about how graphs, graph technology, and Hume can help you tackle them.
So far, the Graph-Powered Machine Learning book has introduced us to graphs and machine learning. The second part of the book talks about recommendations. Recommender systems (RS) gather information about users and items and provide item suggestions, bringing great value to online stores - clothing stores, bookstores, you name it. Companies like Netflix base their entire businesses on high performing recommender systems.
Everyone has a favourite grocery shop they usually go to, maybe the shop close to home, the one with the most competitive prices, the freshest fruit, or simply the best cake. Similarly, everyone may be inclined to buy from one particular e-commerce platform rather than another.
What have we learned from Graph-Powered Machine Learning so far?
Knowledge sharing has always been extremely important for Engineering at GraphAware.Whether it is techniques, tools or technology, lessons learned from our consulting engagements, or experience in general,sharing sparks conversation, creativity and discovery of different or better ways to do things.
