MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce

TL;DR

MRSE is a multi-modality retrieval system for large-scale e-commerce that integrates text, images, and user preferences, significantly improving relevance and online metrics over existing uni-modality systems.

Contribution

It introduces a lightweight mixture-of-expert model and a hybrid loss function to better align multi-modal features and user preferences, enhancing retrieval accuracy.

Findings

18.9% improvement in offline relevance

3.7% gain in online core metrics

Effective integration of multi-modal data and user preferences

Abstract

Providing high-quality item recall for text queries is crucial in large-scale e-commerce search systems. Current Embedding-based Retrieval Systems (ERS) embed queries and items into a shared low-dimensional space, but uni-modality ERS rely too heavily on textual features, making them unreliable in complex contexts. While multi-modality ERS incorporate various data sources, they often overlook individual preferences for different modalities, leading to suboptimal results. To address these issues, we propose MRSE, a Multi-modality Retrieval System that integrates text, item images, and user preferences through lightweight mixture-of-expert (LMoE) modules to better align features across and within modalities. MRSE also builds user profiles at a multi-modality level and introduces a novel hybrid loss function that enhances consistency and robustness using hard negative sampling. Experiments on a large-scale dataset from Shopee and online A/B testing show that MRSE achieves an 18.9% improvement in offline relevance and a 3.7% gain in online core metrics compared to Shopee's state-of-the-art uni-modality system.