@inproceedings{zhong-etal-2026-groclm,
title = "{G}roc{LM}: Grocery Category Recommendation in {E}-Commerce with Large Language Models",
author = "Zhong, Yuan and
Ruan, Chuanwei and
Hasani, Moein and
Tenneti, Tejaswi and
Wang, Haixun and
Ma, Fenglong",
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://aclanthology.org/2026.acl-industry.25/",
pages = "371--385",
ISBN = "979-8-89176-394-4",
abstract = "The rapid growth of online grocery shopping requires recommendation systems that capture cyclical purchasing behavior and diverse user intents. Traditional item-level methods face scalability and accuracy challenges, motivating category-level recommendation as a more structured and practical alternative. We present GrocLM, a fine-tuned language model for grocery category recommendation in a real-world production environment. GrocLM employs a two-stage LoRA-based training strategy to encode cyclical purchasing patterns directly into model parameters, enabling more effective utilization of rebuying signals compared to prompt-based conditioning. To ensure valid and controllable outputs, we further introduce a trie-based constrained decoding mechanism over a predefined category space. Experiments on both proprietary production data and a public benchmark demonstrate that GrocLM consistently outperforms strong baselines. In a live production restocking task, GrocLM achieves a 7.5{\%} relative improvement in cart-adds per impression while maintaining efficient inference by generating all categories jointly. These results highlight the effectiveness and practicality of integrating large language models into structured recommendation systems."
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<abstract>The rapid growth of online grocery shopping requires recommendation systems that capture cyclical purchasing behavior and diverse user intents. Traditional item-level methods face scalability and accuracy challenges, motivating category-level recommendation as a more structured and practical alternative. We present GrocLM, a fine-tuned language model for grocery category recommendation in a real-world production environment. GrocLM employs a two-stage LoRA-based training strategy to encode cyclical purchasing patterns directly into model parameters, enabling more effective utilization of rebuying signals compared to prompt-based conditioning. To ensure valid and controllable outputs, we further introduce a trie-based constrained decoding mechanism over a predefined category space. Experiments on both proprietary production data and a public benchmark demonstrate that GrocLM consistently outperforms strong baselines. In a live production restocking task, GrocLM achieves a 7.5% relative improvement in cart-adds per impression while maintaining efficient inference by generating all categories jointly. These results highlight the effectiveness and practicality of integrating large language models into structured recommendation systems.</abstract>
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%0 Conference Proceedings
%T GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models
%A Zhong, Yuan
%A Ruan, Chuanwei
%A Hasani, Moein
%A Tenneti, Tejaswi
%A Wang, Haixun
%A Ma, Fenglong
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F zhong-etal-2026-groclm
%X The rapid growth of online grocery shopping requires recommendation systems that capture cyclical purchasing behavior and diverse user intents. Traditional item-level methods face scalability and accuracy challenges, motivating category-level recommendation as a more structured and practical alternative. We present GrocLM, a fine-tuned language model for grocery category recommendation in a real-world production environment. GrocLM employs a two-stage LoRA-based training strategy to encode cyclical purchasing patterns directly into model parameters, enabling more effective utilization of rebuying signals compared to prompt-based conditioning. To ensure valid and controllable outputs, we further introduce a trie-based constrained decoding mechanism over a predefined category space. Experiments on both proprietary production data and a public benchmark demonstrate that GrocLM consistently outperforms strong baselines. In a live production restocking task, GrocLM achieves a 7.5% relative improvement in cart-adds per impression while maintaining efficient inference by generating all categories jointly. These results highlight the effectiveness and practicality of integrating large language models into structured recommendation systems.
%U https://aclanthology.org/2026.acl-industry.25/
%P 371-385
Markdown (Informal)
[GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models](https://aclanthology.org/2026.acl-industry.25/) (Zhong et al., ACL 2026)
ACL