mlsanityData to 7 Oct 2026

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

arXiv 2306.03411 ↗First posted 6 Jun 2023cs.CL cs.AI cs.IRcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 44 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

15 of 44 references.

References · 15

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3 more
  1. 2019FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevancecited by 24
  2. 2021Answering Product-Questions by Utilizing Questions from Other Contextually Similar Productscited by 12
  3. 2018Generating Synthetic Data for Neural Keyword-to-Question Modelscited by 1

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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024An Empirical Comparison of Generative Approaches for Product Attribute-Value Identificationcited by 7
  2. 2024Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commercecited by 4
  3. 2024Identifying High Consideration E-Commerce Search Queriescited by 4
  4. 2024Exploring Large Language Models for Product Attribute Value Identificationcited by 3
  5. 2025REIC: RAG-Enhanced Intent Classification at Scalecited by 3
  6. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challengescited by 3