Alibaba International E-commerce Product Search Competition DcuRAGONs Team Technical Report

TL;DR

This paper presents a data-centric approach using Large Language Models for multilingual e-commerce search relevance, achieving top performance in a competitive setting and providing open-source code.

Contribution

The team developed a novel data-centric method leveraging LLMs that outperformed other solutions in the Alibaba International E-commerce Search Competition.

Findings

Achieved the highest score in the competition

Demonstrated effectiveness of LLMs in multilingual search relevance

Published open-source code for reproducibility

Abstract

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.