GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models

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[Submitted on 4 Jun 2026]

Title:GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models

View a PDF of the paper titled GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models, by Yuan Zhong and 5 other authors

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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.

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.24764 [cs.AI]

(or arXiv:2607.24764v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.24764

arXiv-issued DOI via DataCite

Submission history

From: Yuan Zhong [view email] [v1] Thu, 4 Jun 2026 23:55:15 UTC (901 KB)

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