Analyticup E-commerce Product Search Competition Technical Report from Team Tredence_AICOE

TL;DR

This paper describes a multilingual e-commerce search system developed for a competition, utilizing data augmentation and fine-tuning large language models, achieving high accuracy and securing 4th place.

Contribution

The study introduces a multilingual search approach with data augmentation and model fine-tuning, improving relevance task performance in e-commerce search.

Findings

Achieved an average F1-score of 0.8857 on the private test set.

Secured 4th place in the competition leaderboard.

Effective use of data translation and model fine-tuning strategies.

Abstract

This study presents the multilingual e-commerce search system developed by the Tredence_AICOE team. The competition features two multilingual relevance tasks: Query-Category (QC) Relevance, which evaluates how well a user's search query aligns with a product category, and Query-Item (QI) Relevance, which measures the match between a multilingual search query and an individual product listing. To ensure full language coverage, we performed data augmentation by translating existing datasets into languages missing from the development set, enabling training across all target languages. We fine-tuned Gemma-3 12B and Qwen-2.5 14B model for both tasks using multiple strategies. The Gemma-3 12B (4-bit) model achieved the best QC performance using original and translated data, and the best QI performance using original, translated, and minority class data creation. These approaches secured 4th place on the final leaderboard, with an average F1-score of 0.8857 on the private test set.