A Data-Centric Approach to Multilingual E-Commerce Product Search: Case Study on Query-Category and Query-Item Relevance

TL;DR

This paper introduces a data-centric framework that improves multilingual e-commerce search relevance by enhancing training data quality and diversity, outperforming model-centric approaches on key tasks.

Contribution

The work presents a novel, architecture-agnostic data augmentation and filtering strategy to address data imbalance and noise in multilingual search models.

Findings

Significant F1 score improvements on the CIKM AnalytiCup 2025 dataset.

Data engineering can outperform complex model modifications.

Effective handling of low-resource languages and noisy labels.

Abstract

Multilingual e-commerce search suffers from severe data imbalance across languages, label noise, and limited supervision for low-resource languages--challenges that impede the cross-lingual generalization of relevance models despite the strong capabilities of large language models (LLMs). In this work, we present a practical, architecture-agnostic, data-centric framework to enhance performance on two core tasks: Query-Category (QC) relevance (matching queries to product categories) and Query-Item (QI) relevance (matching queries to product titles). Rather than altering the model, we redesign the training data through three complementary strategies: (1) translation-based augmentation to synthesize examples for languages absent in training, (2) semantic negative sampling to generate hard negatives and mitigate class imbalance, and (3) self-validation filtering to detect and remove likely mislabeled instances. Evaluated on the CIKM AnalytiCup 2025 dataset, our approach consistently yields substantial F1 score improvements over strong LLM baselines, achieving competitive results in the official competition. Our findings demonstrate that systematic data engineering can be as impactful as--and often more deployable than--complex model modifications, offering actionable guidance for building robust multilingual search systems in the real-world e-commerce settings.