用大模型提升电商生鲜品类推荐准确率，支持批量生成。
GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models
- 用两阶段LoRA微调将周期性购买模式注入模型参数。
- 线上实验中每曝光加购率提升7.5%，优于基线。
- 基于前缀树约束解码，确保输出符合预设品类结构。
在线生鲜购物的快速增长要求推荐系统能捕捉周期性购买行为和多样化的用户意图。传统基于商品级别的方法存在可扩展性和准确性挑战，促使品类级别推荐成为更结构化且实用的替代方案。我们提出GROCLM，一个在真实生产环境中用于生鲜品类推荐的微调语言模型。GROCLM采用两阶段基于LoRA的训练策略，将周期性购买模式直接编码到模型参数中，相比提示工程方式更有效利用复购信号。为确保输出合法且可控，我们引入基于前缀树的约束解码机制，在预定义品类空间内进行生成。在自有生产数据和公开基准上的实验表明，GROCLM持续优于强基线。在实际补货任务中，其每曝光加购数提升7.5%，同时通过联合生成所有品类保持高效推理。结果凸显了大语言模型融入结构化推荐系统的有效性与实用性。
原文摘要 · Abstract (English)
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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