Alibaba International E-commerce Product Search Competition DcuRAGONs Team Technical Report
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 29 Oct 2025.
Builds on
5 of 6 references.
References · 5
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2025Qwen3 Technical Reportcited by 8,820
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
- 2025Gemma 3 Technical Reportcited by 1,824
Built on by
Not cited yet.