@inproceedings{chen-etal-2023-generate,
    title = "Generate-then-Retrieve: Intent-Aware {FAQ} Retrieval in Product Search",
    author = "Chen, Zhiyu  and
      Choi, Jason  and
      Fetahu, Besnik  and
      Rokhlenko, Oleg  and
      Malmasi, Shervin",
    editor = "Sitaram, Sunayana  and
      Beigman Klebanov, Beata  and
      Williams, Jason D",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-industry.73",
    doi = "10.18653/v1/2023.acl-industry.73",
    pages = "763--771",
    abstract = "Frequently Asked Question (FAQ) retrieval aims at retrieving question-answer pairs for a given a user query. Integrating FAQ retrieval with product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support. Providing FAQ content without disrupting user{'}s shopping experience poses challenges on deciding when and how to show FAQ results. Our proposed intent-aware FAQ retrieval consists of (1) an intent classifier that predicts whether the query is looking for an FAQ; (2) a reformulation model that rewrites query into a natural question. Offline evaluation demonstrates that our approach improves 12{\%} in Hit@1 on retrieving ground-truth FAQs, while reducing latency by 95{\%} compared to baseline systems. These improvements are further validated by real user feedback, where more than 99{\%} of users consider FAQs displayed on top of product search results is helpful. Overall, our findings show promising directions for integrating FAQ retrieval into product search at scale.",
}

Frequently Asked Question (FAQ) retrieval aims at retrieving question-answer pairs for a given a user query. Integrating FAQ retrieval with product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support. Providing FAQ content without disrupting user{'}s shopping experience poses challenges on deciding when and how to show FAQ results. Our proposed intent-aware FAQ retrieval consists of (1) an intent classifier that predicts whether the query is looking for an FAQ; (2) a reformulation model that rewrites query into a natural question. Offline evaluation demonstrates that our approach improves 12{\%} in Hit@1 on retrieving ground-truth FAQs, while reducing latency by 95{\%} compared to baseline systems. These improvements are further validated by real user feedback, where more than 99{\%} of users consider FAQs displayed on top of product search results is helpful. Overall, our findings show promising directions for integrating FAQ retrieval into product search at scale.
Chen, Zhiyu Choi, Jason Fetahu, Besnik Rokhlenko, Oleg Malmasi, Shervin
Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search