Real-Time Recommendation Engines

In a retail system it is essential to maximizing customer value and staying competitive. The recommendations must be inductive and contextual.

Graph database efficiently tracks the relationships between buyer and product data according to the user purchase,interactions, and reviews to give you the most meaningful insight into customer needs and product trends.

Graph-powered recommendation engines can take two major approaches:

*identifying resources of interest to individuals; or

*identifying individuals likely to be interested in a given resource.

With either approach, graph databases make the necessary correlations and connections to serve up the most relevant results for the individual or resource in question.

Retail industry leader Walmart has decided to use a graph database to serve up real-time product recommendations by using information about what users prefer.

For more details on Real-time recommendation engines see https://neo4j.com/blog/enterprise-real-time-recommendation-engines/.