Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search

TL;DR

This paper introduces an intent-aware FAQ retrieval system for product search that improves accuracy and reduces latency, enhancing user experience and support with positive real-world feedback.

Contribution

It presents a novel intent classifier and query reformulation model that together improve FAQ retrieval effectiveness and efficiency in product search environments.

Findings

Hit@1 improved by 13% for FAQ retrieval

Latency reduced by 95% compared to baselines

71% of FAQs received positive user feedback

Abstract

Customers interacting with product search engines are increasingly formulating information-seeking queries. Frequently Asked Question (FAQ) retrieval aims to retrieve common question-answer pairs for a user query with question intent. Integrating FAQ retrieval in product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support. Determining when an FAQ entry can satisfy a user's information need within product search, without disrupting their shopping experience, represents an important challenge. We propose an intent-aware FAQ retrieval system consisting of (1) an intent classifier that predicts when a user's information need can be answered by an FAQ; (2) a reformulation model that rewrites a query into a natural question. Offline evaluation demonstrates that our approach improves Hit@1 by 13% on retrieving ground-truth FAQs, while reducing latency by 95% compared to baseline systems. These improvements are further validated by real user feedback, where 71% of displayed FAQs on top of product search results received explicit positive user feedback. Overall, our findings show promising directions for integrating FAQ retrieval into product search at scale.