GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models
Yuan Zhong, Chuanwei Ruan, Moein Hasani, Tejaswi Tenneti, Haixun Wang, Fenglong Ma
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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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表