  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.
