Abstract
As global e-commerce platforms continue to expand, companies are entering new markets where they encounter cold-start challenges due to limited human labels and user behaviors. In this paper, we share our experiences in Coupang to provide a competitive cold-start performance of relevance matching for emerging e-commerce markets. Specifically, we present a Cold-Start Relevance Matching (CSRM) framework, utilizing a multilingual Large Language Model (LLM) to address three challenges: (1) activating cross-lingual transfer learning abilities of LLMs through machine translation tasks; (2) enhancing query understanding and incorporating e-commerce knowledge by retrieval-based query augmentation; (3) mitigating the impact of training label errors through a multi-round self-distillation training strategy. Our experiments demonstrate the effectiveness of CSRM-LLM and the proposed techniques, resulting in successful real-world deployment and significant online gains, with a 45.8% reduction in defect ratio and a 0.866% uplift in session purchase rate.
Abstract (translated)
随着全球电子商务平台的不断扩展，公司进入新市场时面临着冷启动挑战，这些挑战主要是由于缺乏人类标注和用户行为数据。本文分享了Coupang在新兴电子商务市场的相关性匹配中实现竞争力的冷启动性能的经验。具体而言，我们提出了一种冷启动相关性匹配（CSRM）框架，该框架利用多语言大型语言模型（LLM）来解决三个挑战：(1) 通过机器翻译任务激活LLM的跨语言迁移学习能力；(2) 通过基于检索的查询增强提高查询理解，并将电子商务知识融入其中；(3) 通过多次自我蒸馏训练策略减轻训练标签错误的影响。我们的实验表明，CSRM-LLM及其提出的技巧非常有效，实现了成功的真实世界部署和显著的在线收益，包括缺陷率降低了45.8%，会话购买率提高了0.866%。
URL
https://arxiv.org/abs/2509.01566
