Graph Advantage: Real-Time Recommendations

We all receive recommendations presented to us on a daily basis. From the products that we should be buying to the movies we should be watching to the people we should be dating…the list goes on. You’re capable of recommending just about anything — as long as you have the right data in place. Graph databases are naturally well-suited for building real-time recommendation engines thanks to the native graph traversal performance when traversing the network around and between the desired starting node such as a person that already bought a set of products.

Whether an enterprise functions within the social, media or retail sector, providing users with targeted, real-time recommendations are important for providing the customer value through a personalized experience, which is quickly becoming the baseline for remaining competitive. Unlike that of business data, recommendations should be contextual and inductive so it can be deemed relevant by the end consumer. Achieving this requires a “good enough” level of data classification with sufficient connectedness between the data points in the system.

With a graph database where relationships are treated as first class citizens, you can connect a customer’s browsing history while combining that with their purchase history and offline product and brand interactions to enable the real-time recommendation algorithm to utilize their present choices and offer personalized recommendations without any offline pre-compute delaying the interaction — lowering the potential for the consumer to purchase from a competitor.

Neo4j for Real-Time Recommendations

Whether you’re leveraging social connections or connecting data across digital and physical customer touch points, the Neo4j graph database provides the possibility of providing relevant real-time recommendations 

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