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

• URL: http://arxiv.org/abs/2607.24764v1
• Date: Thu, 04 Jun 2026 23:55:15 GMT
• Title: GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models
• Abstract summary: We present a fine-tuned language model for grocery category recommendation in a real-world production environment.<n>GROCLM employs a two-stage LoRA-based training strategy to encode cyclical purchasing patterns directly into model parameters.<n>Experiments on both proprietary production data and a public benchmark demonstrate that GROCLM consistently outperforms strong baselines.
• Score: 31.736819236884827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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