@inproceedings{yu-etal-2026-taotype,
title = "{T}ao{T}ype: Predicting Fine-Grained Typing Intent for Faster Search",
author = "Yu, Yipeng and
Yuan, Yichen and
Feng, Chengxiao and
Liu, Xu",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.13/",
pages = "184--192",
ISBN = "979-8-89176-394-4",
abstract = "``Is the user{'}s current query input exactly what they intend to search for?'' Our work aims to answer this question by determining, at each typing, whether the current query is complete. If so, a search is implicitly triggered in advance without waiting for user confirmation. This approach reduces response time and enhances the user search experience. Specifically, we propose TaoType, a client-side framework that introduces innovation in data sampling, feature selection, model design and training, and online strategy. Experiments in a leading mobile shopping application named Taobao validate its effectiveness, achieving offline precision/recall/accuracy of 0.7936/0.8196/0.7742, respectively, and decreasing online response time by 640.51{\ensuremath{\pm}}93.65 milliseconds, which is of great benefit to the search system. Unlike prior work that focuses on optimizing server-side engineering pipelines or simplifying ranking models, our method leverages client-side typing behavior for real-time early prediction, utilizing on-device computation to gain response time reducing. To the best of our knowledge, our work is the first to identify and address this problem. This work also introduces App Intelligence, a new paradigm for enhancing mobile applications by integrating on-device AI to boost business value and user experience."
}Markdown (Informal)
[TaoType: Predicting Fine-Grained Typing Intent for Faster Search](https://preview.aclanthology.org/ingest-acl/2026.acl-industry.13/) (Yu et al., ACL 2026)
ACL