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Solution Accelerator

Building Common Sense Product Recommendations With LLMs

Pre-built code, sample data and step-by-step instructions ready to go in a Databricks Notebook

Building common sense product recommendations with Large Language Models (LLMs)
Building common sense product recommendations with Large Language Models (LLMs)

Deliver intuitive product recommendations that drive customer journeys

Product recommendations play a central role in guiding customers through their shopping journey with tailored suggestions based on their buying behaviors and preferences. With large language models (LLMs), retailers can automate the delivery of personalized suggestions that adapt to evolving customer preferences — enhancing user engagement, increasing sales and fostering long-term customer loyalty.

Use this Solution Accelerator to develop product recommendations based on common sense linkages for new-to-market products and optimized recommendation engines:

  • Convert all of your specific product descriptions and metadata into embeddings and store them in a searchable index
  • Task an LLM to recommend products based on their connection to other relevant products
Download notebook

Resources

Automating product review summarization with Large Language Models (LLMs)

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Commonsense Product Recommendations Using Large Language Models

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Retail in the Age of Generative AI

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Retail in the Age of Generative AI

10 ways large language models (LLMs) may impact the retail industry

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Fine-Tuning Large Language Models with Hugging Face and DeepSpeed

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Fine-Tuning Large Language Models With Hugging Face and DeepSpeed

Easily apply and customize large language models of billions of parameters

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