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.