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.

🔗 Launch Solution Accelerator 👈