Exploring Query Understanding for Amazon Product Search

TL;DR

This paper investigates the impact of query understanding on Amazon product search, analyzing how it influences ranking features and performance, and proposes a multi-task learning framework to enhance search ranking accuracy.

Contribution

It provides a comprehensive real-world study of query understanding's role in product search and introduces a novel multi-task learning framework for improved ranking.

Findings

Query understanding-based features significantly influence ranking quality.

The query understanding system enhances the performance of ranking models.

A new multi-task learning framework improves ranking accuracy.

Abstract

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.