# Learning variant product relationship and variation attributes from e-commerce website structures

arXiv: https://arxiv.org/abs/2410.02779 · HTML: https://mlsanity.com/paper/2410.02779/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce](https://mlsanity.com/paper/2009.02835/) (2020)
- [Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey](https://mlsanity.com/paper/2406.08068/) (2024)
- [Deep Indexed Active Learning for Matching Heterogeneous Entity Representations](https://mlsanity.com/paper/2104.03986/) (2021)
- [BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search](https://mlsanity.com/paper/2010.10442/) (2020)

## Built on by (most cited first, 4 of 4)

- [Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs](https://mlsanity.com/paper/2510.23941/) (2025)
- [Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping](https://mlsanity.com/paper/2509.01182/) (2025)
- [Optimizing Product Deduplication in E-Commerce with Multimodal Embeddings](https://mlsanity.com/paper/2509.15858/) (2025)
- [Positional task conditioning for scalable defect detection across product families in large product catalogs](https://mlsanity.com/paper/2609.09567/) (2026)

## Abstract (arXiv)

We introduce VARM, variant relationship matcher strategy, to identify pairs of variant products in e-commerce catalogs. Traditional definitions of entity resolution are concerned with whether product mentions refer to the same underlying product. However, this fails to capture product relationships that are critical for e-commerce applications, such as having similar, but not identical, products listed on the same webpage or share reviews. Here, we formulate a new type of entity resolution in variant product relationships to capture these similar e-commerce product links. In contrast with the traditional definition, the new definition requires both identifying if two products are variant matches of each other and what are the attributes that vary between them. To satisfy these two requirements, we developed a strategy that leverages the strengths of both encoding and generative AI models. First, we construct a dataset that captures webpage product links, and therefore variant product relationships, to train an encoding LLM to predict variant matches for any given pair of products. Second, we use RAG prompted generative LLMs to extract variation and common attributes amongst groups of variant products. To validate our strategy, we evaluated model performance using real data from one of the world's leading e-commerce retailers. The results showed that our strategy outperforms alternative solutions and paves the way to exploiting these new type of product relationships.