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  • Kyle McNamara

Leveraging Dimensionality for Graph-Based Recommendations with Sparse Data

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.


Youtube (presentation starts at about :50 seconds after an ad/intro):

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

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


Graphable delivers insightful graph database (e.g. Neo4j) / machine learning (ml) / natural language processing (nlp) projects as well as graph and traditional 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.

Contact us for more information: info [at] graphable [dot] ai | Tel: +1 844-472-7471

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