Graph Neural Networks can be used for a variety of applications but do you know what it takes to create a great recommendation system? Dive deep into the math of GNNs, implement a link prediction module and show everyone how stunning graph machine learning can be!
It’s true every recommendation engine requires a performant database to analyze the data and provide the recommendation, but why exactly does Memgraph stand out? Easy - C++, in-memory, real-time analytics! Three things to change the recommendation game.
If a recommendation engine built on relational databases is falling a part due to the bottlenecks made by complex JOINs and never-ending schema changes, there is only one permanent and game changing solution - graph databases.
Movie ratings from MovieLens are incoming, but you are still not sure what to watch over the weekend? Create your own movie recommendation system.
Temporal graph neural networks can be used to perform both label classification and link prediction. Learn how to create a simple graph recommendation engine using TGNs on an Amazon product dataset.
Learn how graph databases can offer powerful data modeling and analysis capabilities your business can leverage to easily model real-world complex systems and answer challenging questions.