Graph + AI World - Recommendation Engine

Recommendation Engine with In-Database Machine Learning

Graph + AI World Session - Recorded September 2020

Recommendation systems are utilized in a variety of services, such as video streaming, online shopping, and social media. For industrial applications, the database can hold hundreds of millions of users and items. In-database model training also avoids exporting the graph data from the DBMS to other machine learning platforms and thus better support continuous recommendation model update over evolving training data.

About TigerGraph

TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions.

Graph + AI World:

Recommendation Engine with In-Database Machine Learning

Graph + AI World Session - Recorded September 2020

Watch the Session

“TigerGraph is highly configurable ands can fit your specific use case. Whether you’re trying to get super fast query times or run heavy graph processing algorithms, setting the right configuration on your cluster will make all the difference.”

Xandr

About TigerGraph