利用大模型提升多语言电商搜索相关性，竞赛中表现最优。
Alibaba International E-commerce Product Search Competition DcuRAGONs Team Technical Report
- 基于数据驱动方法，融合大模型能力优化搜索匹配
- 在多语言电商搜索任务中取得竞赛最高分
- 适合关注大模型应用与电商搜索的开发者
本报告详细介绍了我们在多语言电商搜索竞赛中提出的方法与成果。该任务旨在识别用户查询与商品项目之间的相关性，以提升电商平台的推荐性能。我们采用大语言模型（LLMs）在其他任务中的能力，构建了以数据为中心的方法，在竞赛中取得了最高得分。最终排行榜已公布于 https://alibaba-international-cikm2025.github.io。项目源代码发布于 https://github.com/nhtlongcs/e-commerce-product-search。
原文摘要 · Abstract (English)
This report details our methodology and results developed for the Multilingual E-commerce Search Competition. The problem aims to recognize relevance between user queries versus product items in a multilingual context and improve recommendation performance on e-commerce platforms. Utilizing Large Language Models (LLMs) and their capabilities in other tasks, our data-centric method achieved the highest score compared to other solutions during the competition. Final leaderboard is publised at https://alibaba-international-cikm2025.github.io. The source code for our project is published at https://github.com/nhtlongcs/e-commerce-product-search.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。