ToolsThe story, in brief

Using graph neural networks to recommend related products

160% improvement. That's what Amazon achieved using graph neural networks for product recommendations.

Illustration of a transparent lens revealing connected networks across layers of paper.
Exploring the next frontier of AI research.AI illustration by KeyNews
The KeyNews take

Why it matters

Amazon's research demonstrates significant performance gains in recommendation systems using advanced AI techniques, showing concrete metrics that other e-commerce and platform companies can benchmark against.

The key facts

4 to know
  1. 30% to 160% improvements over predecessors

  2. Dual embeddings approach

  3. Graph neural networks for recommendations

  4. Novel loss function implementation

Go to the source

Amazon Scienceamazon.science

Publisher excerpt: Dual embeddings of each node, as both source and target, and a novel loss function enable 30% to 160% improvements over predecessors.
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