Product recommendations help guide customers through their shopping journey. The Building Common Sense Product Recommendations With LLMs Solution Accelerator shows how retailers can use LLMs to develop recommendations for new-to-market products by turning product descriptions and metadata into embeddings, storing them in a searchable index, and using an LLM to recommend related products.
With this Accelerator, you get
- Ready-to-use resources: pre-built code, sample data and step-by-step instructions ready to go in a Databricks notebook
- Create embeddings from product data: convert product descriptions and metadata into embeddings.
- Build a searchable index: store product information in a format that can be searched efficiently.
- Recommend related products: use an LLM to suggest products based on their connection to other relevant items.
- Support recommendation use cases for new products: apply common sense linkages where historical behavior may be limited.
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