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Recommendation.md

Personalized Product Recommendations with Neo4j

Recommendations

Personalized product recommendations can increase conversions, improve sales rates and provide a better experice for users. In this Neo4j Browser guide, we’ll take a look at how you can generate graph-based real-time personalized product recommendations using a dataset of movies and movie ratings, but these techniques can be applied to many different types of products or content

Graph-Based Recommendations

Generating personalized recommendations is one of the most common use cases for a graph database. Some of the main benefits of using graphs to generate recommendations include

  • Performance. Index-free adjacency allows for calculating recommendations in real time, ensuring the recommendation is always relevant and reflecting up-to-date information.
  • Data model. The labeled propety graph model allows for easily combining datasets from multiple sources, allowing enterprises to unlock value from previously separated data silos. alt text
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