Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search
Zhiyu Chen, Jason Choi, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi

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…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Expert finding and Q&A systems · Advanced Text Analysis Techniques
