An ability to offer complementary product or service recommendations instantly is essential in many scenarios.Recommendation systems need to quickly understand the profile of their client, align that with the rapidly changing profiles of the larger customer base and product catalog, and produce engaging, personalized recommendations.
Using a graph for recommendation analytics is the first step towards faster product and service recommendations. Native Parallel graphs, such as TigerGraph, are built to understand, explore and analyze the complex relationships in the eCommerce data allowing data scientists and business users to go 10 or more levels deep into the data.
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.
TigerGraph is the only system today that can help us make real-time care-path recommendations using knowledge of 50 million patients. Your products will have worldwide impact on making everyone’s lives better in more ways than you can imagine.