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Related Experiment Video

Updated: Mar 1, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Unified AI Approach Using Encoding and Generative Large Language Models for Variant Product Matching in e-Commerce.

Pedro Herrero-Vidal¹, You-Lin Chen¹, Cris Liu¹

¹Amazon.com Inc, Seattle, Washington, USA.

|February 28, 2026

Summary

We developed a variant relationship matcher strategy (VARM) to identify similar products in e-commerce. This approach accurately links variant products and their differing attributes, improving product discovery and data organization.

Keywords:

GenAI Generative Artificial Intelligence LLM e-commerce entity resolution large language models

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Published on: June 13, 2025

Area of Science:

Artificial Intelligence
E-commerce Technology
Data Science

Background:

Traditional entity resolution focuses on identical product matches, neglecting crucial variant relationships in e-commerce.
Identifying similar, non-identical products is vital for applications like shared reviews and webpage listings.

Purpose of the Study:

To introduce a novel entity resolution strategy for variant product relationships.
To accurately identify variant product pairs and their distinguishing attributes in e-commerce catalogs.

Main Methods:

Developed the variant relationship matcher (VARM) strategy.
Constructed a dataset of webpage product links to train an encoding large language model (LLM).
Employed retrieval-augmented generation-prompted generative LLMs to extract product variations and commonalities.

Main Results:

VARM successfully identifies variant product matches and their varying attributes.
The strategy outperforms alternative solutions in real-world e-commerce data.
Demonstrated the effectiveness of combining encoding and generative AI for this task.

Conclusions:

VARM establishes a new standard for e-commerce entity resolution by capturing variant product links.
This approach enables better exploitation of complex product relationships.
The findings have significant implications for e-commerce data management and user experience.