# A Usage-centric Take on Intent Understanding in E-Commerce

arXiv: https://arxiv.org/abs/2402.14901 · HTML: https://mlsanity.com/paper/2402.14901/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery](https://mlsanity.com/paper/2211.08316/) (2022)
- [Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph](https://mlsanity.com/paper/2209.15214/) (2022)

## Built on by (most cited first, 6 of 6)

- [IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce](https://mlsanity.com/paper/2406.10173/) (2024)
- [MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding](https://mlsanity.com/paper/2406.10701/) (2024)
- [RECAP: REwriting Conversations for Intent Understanding in Agentic Planning](https://mlsanity.com/paper/2509.04472/) (2025)
- [Intention Knowledge Graph Construction for User Intention Relation Modeling](https://mlsanity.com/paper/2412.11500/) (2024)
- [Genesis: Evolving Attack Strategies for LLM Web Agent Red-Teaming](https://mlsanity.com/paper/2510.18314/) (2025)
- [Performance Trade-offs of Optimizing Small Language Models for E-Commerce](https://mlsanity.com/paper/2510.21970/) (2025)

## Abstract (arXiv)

Identifying and understanding user intents is a pivotal task for E-Commerce. Despite its essential role in product recommendation and business user profiling analysis, intent understanding has not been consistently defined or accurately benchmarked. In this paper, we focus on predicative user intents as "how a customer uses a product", and pose intent understanding as a natural language reasoning task, independent of product ontologies. We identify two weaknesses of FolkScope, the SOTA E-Commerce Intent Knowledge Graph: category-rigidity and property-ambiguity. They limit its ability to strongly align user intents with products having the most desirable property, and to recommend useful products across diverse categories. Following these observations, we introduce a Product Recovery Benchmark featuring a novel evaluation framework and an example dataset. We further validate the above FolkScope weaknesses on this benchmark. Our code and dataset are available at https://github.com/stayones/Usgae-Centric-Intent-Understanding.