CSRM-LLM：拥抱多语言LLM以应对新兴电商市场中的冷启动相关性匹配
CSRM-LLM: Embracing Multilingual LLMs for Cold-Start Relevance Matching in Emerging E-commerce Markets
AI总结：
针对新兴电商市场的冷启动相关性匹配挑战，提出CSRM-LLM框架，利用多语言LLM结合机器翻译、检索式查询增强和多轮自蒸馏技术，实现显著线上收益并成功部署。
摘要：
随着全球电商平台不断扩张，企业在进入新市场时，由于有限的人工标注和用户行为数据，面临着冷启动挑战。在本文中，我们分享了在Coupang的经验，旨在为新兴电商市场提供具有竞争力的冷启动相关性匹配性能。具体而言，我们提出了一个冷启动相关性匹配（CSRM）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，改善查询理解并融入电商知识；（3）通过多轮自蒸馏训练策略，减轻训练标签错误的影响。我们的实验证明了CSRM-LLM及所提技术的有效性，实现了成功的真实世界部署和显著的线上收益，缺陷率降低了45.8%，会话购买率提升了0.866%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
