Overview
This Guidance demonstrates how retailers can use Amazon OpenSearch Service, in combination with natural language processing, to create digital recommendations when needing to replace out-of-stock store products. Product names and descriptions are embedded and stored in a k-nearest neighbors (k-NN) index. When a consumer is querying for product recommendations, neighboring products are located within the k-NN index and returned to the consumer. The relevance of returned products can increase by using the optional category and price-based filters.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Well-Architected Pillars
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible
Deploy with confidence
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.
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