@inproceedings{zhang-etal-2025-hiergr,
title = "{H}ier{GR}: Hierarchical Semantic Representation Enhancement for Generative Retrieval in Food Delivery Search",
author = "Zhang, Fuwei and
Liu, Xiaoyu and
Jia, Xinyu and
Zhang, Yingfei and
Xia, Zenghua and
Jiang, Fei and
Zhuang, Fuzhen and
Lin, Wei and
Zhang, Zhao",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.31/",
doi = "10.18653/v1/2025.acl-industry.31",
pages = "444--455",
ISBN = "979-8-89176-288-6",
abstract = "Food delivery search aims to quickly retrieve deliverable items that meet users' needs, typically requiring faster and more accurate query understanding compared to traditional e-commerce search. Generative retrieval (GR), an emerging search paradigm, harnesses the advanced query understanding capabilities of large language models (LLMs) to enhance the retrieval of results for complex and long-tail queries in food delivery search scenarios. However, there are still challenges in deploying GR to online scenarios: 1) **the large scale of items**; 2) **latency constraints unmet by LLM inference in online retrieval**; and 3) **strong location-based service restrictions on generated items**. To explore the application of GR in food delivery search, we optimize both offline training and online deployment, proposing **Hier**archical semantic representation enhancement for **G**enerative **R**etrieval (HierGR). Specifically, for the generation of semantic IDs, we propose an optimization method that refines the residual quantization process to generate hierarchically semantic IDs for items. Additionally, to successfully deploy on a well-known food delivery platform, we utilize the query cache mechanism and integrate the GR model with the online dense retrieval model to fulfill real-world search requirements. Online A/B testing results show that our proposed method increases **the number of online orders by 0.68{\%}** for complex search intents. The source code is available at https://github.com/zhangfw123/HierGR."
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<abstract>Food delivery search aims to quickly retrieve deliverable items that meet users’ needs, typically requiring faster and more accurate query understanding compared to traditional e-commerce search. Generative retrieval (GR), an emerging search paradigm, harnesses the advanced query understanding capabilities of large language models (LLMs) to enhance the retrieval of results for complex and long-tail queries in food delivery search scenarios. However, there are still challenges in deploying GR to online scenarios: 1) **the large scale of items**; 2) **latency constraints unmet by LLM inference in online retrieval**; and 3) **strong location-based service restrictions on generated items**. To explore the application of GR in food delivery search, we optimize both offline training and online deployment, proposing **Hier**archical semantic representation enhancement for **G**enerative **R**etrieval (HierGR). Specifically, for the generation of semantic IDs, we propose an optimization method that refines the residual quantization process to generate hierarchically semantic IDs for items. Additionally, to successfully deploy on a well-known food delivery platform, we utilize the query cache mechanism and integrate the GR model with the online dense retrieval model to fulfill real-world search requirements. Online A/B testing results show that our proposed method increases **the number of online orders by 0.68%** for complex search intents. The source code is available at https://github.com/zhangfw123/HierGR.</abstract>
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%0 Conference Proceedings
%T HierGR: Hierarchical Semantic Representation Enhancement for Generative Retrieval in Food Delivery Search
%A Zhang, Fuwei
%A Liu, Xiaoyu
%A Jia, Xinyu
%A Zhang, Yingfei
%A Xia, Zenghua
%A Jiang, Fei
%A Zhuang, Fuzhen
%A Lin, Wei
%A Zhang, Zhao
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F zhang-etal-2025-hiergr
%X Food delivery search aims to quickly retrieve deliverable items that meet users’ needs, typically requiring faster and more accurate query understanding compared to traditional e-commerce search. Generative retrieval (GR), an emerging search paradigm, harnesses the advanced query understanding capabilities of large language models (LLMs) to enhance the retrieval of results for complex and long-tail queries in food delivery search scenarios. However, there are still challenges in deploying GR to online scenarios: 1) **the large scale of items**; 2) **latency constraints unmet by LLM inference in online retrieval**; and 3) **strong location-based service restrictions on generated items**. To explore the application of GR in food delivery search, we optimize both offline training and online deployment, proposing **Hier**archical semantic representation enhancement for **G**enerative **R**etrieval (HierGR). Specifically, for the generation of semantic IDs, we propose an optimization method that refines the residual quantization process to generate hierarchically semantic IDs for items. Additionally, to successfully deploy on a well-known food delivery platform, we utilize the query cache mechanism and integrate the GR model with the online dense retrieval model to fulfill real-world search requirements. Online A/B testing results show that our proposed method increases **the number of online orders by 0.68%** for complex search intents. The source code is available at https://github.com/zhangfw123/HierGR.
%R 10.18653/v1/2025.acl-industry.31
%U https://aclanthology.org/2025.acl-industry.31/
%U https://doi.org/10.18653/v1/2025.acl-industry.31
%P 444-455
Markdown (Informal)
[HierGR: Hierarchical Semantic Representation Enhancement for Generative Retrieval in Food Delivery Search](https://aclanthology.org/2025.acl-industry.31/) (Zhang et al., ACL 2025)
ACL
- Fuwei Zhang, Xiaoyu Liu, Xinyu Jia, Yingfei Zhang, Zenghua Xia, Fei Jiang, Fuzhen Zhuang, Wei Lin, and Zhao Zhang. 2025. HierGR: Hierarchical Semantic Representation Enhancement for Generative Retrieval in Food Delivery Search. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 444–455, Vienna, Austria. Association for Computational Linguistics.