GraphAware Blog

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Application state as a graph

Application state as a graph

19 Jun 2022 by Jiri Palas · 3 min read Engineering Front-end

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?

Session-based recommendations with Graphs

Session-based recommendations with Graphs

14 Jun 2022 by Alexandra Klacanova · 6 min read Beginner business Recommendations

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.

Intro to collaborative filtering

Intro to collaborative filtering

12 May 2022 by Alexandra Klacanova · 7 min read Beginner business Recommendations

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.

What are content-based recommendations?

What are content-based recommendations?

19 Apr 2022 by Alexandra Klacanova · 7 min read Beginner business Recommendations

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.

Optimise Logistics Chains with Graphs and Hume

Optimise Logistics Chains with Graphs and Hume

14 Apr 2022 by Federica Ventruto · 11 min read Hume

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.

Communities at GraphAware

Communities at GraphAware

17 Mar 2022 by Luanne Misquitta · 4 min read Engineering

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.