Leveraging Dimensionality for Graph-Based Recommendations with Sparse Data

By Will Evans / VP of Consulting

October 28, 2020

Blog

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Video of online presentation from Will Evans, Graphable’s VP of Strategy & Innovation, presenting at Neo4j Nodes2020. The presentation covers a helpful approach for working with dimensions in graphs for maximizing relevancy in recommendations / recommendation engines, when leveraging graph databases.

The video also includes a demonstration of the most efficient way of managing Graph/Analytics/DataScience projects leveraging the GraphAware Hume platform.


Graphable delivers insightful graph database (e.g. Neo4j consulting) / machine learning (ml) / natural language processing (nlp) projects as well as graph and Domo consulting for BI/analytics, with measurable impact. We are known for operating ethically, communicating well, and delivering on-time. With hundreds of successful projects across most industries, we thrive in the most challenging data integration and data science contexts, driving analytics success.

Want to find out more about our Hume consulting on the Hume knowledge graph / insights platform? As the Americas principal reseller, we are happy to connect and tell you more. Book a demo by contacting us here.

Check out our article, What is a Graph Database? for more info.


We are known for operating ethically, communicating well, and delivering on-time. With hundreds of successful projects across most industries, we thrive in the most challenging data integration and data science contexts, driving analytics success.
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