Read this white paper to find out about graph-based approaches to recommendations.

Graph Data Science Use Cases: Recommendations

Available Formats: PDF - EN US


Insightful recommendations drive customer satisfaction – and revenue. So how can you deliver the very best recommendations, no matter how extensive your product catalog?

Graph-based approaches pull in domain-specific knowledge of your product offering as well as user actions and behavior using the connections in your data. Neo4j Graph Data Science empowers you to drive accurate recommendations and higher conversion rates.

In this brief paper, you will:

  • Learn three techniques for increasing recommendation accuracy
  • See a sample graph data model
  • Find out which graph algorithms to run – and why
  • Discover how a top retailer drives recommendations with customer interactions, product taxonomies, search queries, and more

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