GrocLM：使用大语言模型进行电子商务中的杂货类别推荐

中文摘要 AI 辅助

在线杂货购物的快速增长需要能够捕捉周期性购买行为和多样用户意图的推荐系统。传统的商品级方法面临可扩展性和准确性挑战，促使类别级推荐成为更具结构性和实用性的选择。我们提出了GROCLM，一种在实际生产环境中用于杂货类别推荐的微调语言模型。GROCLM采用基于LoRA的两阶段训练策略，将周期性购买模式直接编码到模型参数中，与基于提示的条件设定相比，能更有效地利用再购买信号。为确保有效和可控的输出，我们在预定义类别空间上进一步引入基于前缀树的约束解码机制。在专有生产数据和公共基准上的实验表明，GROCLM始终优于强大的基线。在实时生产补货任务中，GROCLM在每次展示的购物车添加量上实现了7.5%的相对提升，同时通过联合生成所有类别保持了高效推理。这些结果凸显了将大语言模型集成到结构化推荐系统中的有效性和实用性。

英文摘要

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.

发表机构

• The Pennsylvania State University（宾夕法尼亚州立大学）
• Instacart（因斯塔卡特公司）
• Evenup（伊文纳普公司）

机构由 AI 辅助整理，请以论文原文为准。