# Exploring Query Understanding for Amazon Product Search

arXiv: https://arxiv.org/abs/2408.02215 · HTML: https://mlsanity.com/paper/2408.02215/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2024.

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

- [Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models](https://mlsanity.com/paper/2505.07105/) (2025)
- [Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems](https://mlsanity.com/paper/2509.09690/) (2025)
- [Query Brand Entity Linking in E-Commerce Search](https://mlsanity.com/paper/2502.01555/) (2025)
- [Fine-grained auxiliary learning for real-world product recommendation](https://mlsanity.com/paper/2510.04551/) (2025)
- [A Unified Structured Query Understanding Framework for Industrial Semantic Search](https://mlsanity.com/paper/2605.27441/) (2026)
- [Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn](https://mlsanity.com/paper/2607.24783/) (2026)

## Abstract (arXiv)

Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique characteristics, primarily featuring short queries which are mostly a combination of product attributes and structured product search space. The uniqueness of product search underscores the crucial importance of the query understanding component. However, there are limited studies focusing on exploring this impact within real-world product search engines. In this work, we aim to bridge this gap by conducting a comprehensive study and sharing our year-long journey investigating how the query understanding service impacts Amazon Product Search. Firstly, we explore how query understanding-based ranking features influence the ranking process. Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model. Building on the insights gained from our study on the evaluation of the query understanding-based ranking model, we propose a query understanding-based multi-task learning framework for ranking. We present our studies and investigations using the real-world system on Amazon Search.