Recommendation Engine

How many recommendation algorithms do you need for a successful recommendation engine? Start with these five and we'll recommend you more!

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.

DRS is a web application that helps to navigate through documentation by giving you page recommendations based on your input URL.

Movie ratings from MovieLens are incoming, but you are still not sure what to watch over the weekend? Create your own movie recommendation system.

Recommending books is hard. Thanks to the Amazon books dataset and insights from graph analytics, you can build your own. Here’s how.

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 to create a real-time recommendation engine with the Online Node2Vec embedding algorithm

Check out what it takes to develop a recommendation engine with graph analytics

Learn about the benefits of using a graph database instead of SQL in terms of data modeling, querying, and development speed and flexibility.

Learn how to build a recommendation engine with Memgraph using an objective approach.

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.