MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce
<|reference_start|>MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce: 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.<|reference_end|>
@article{jiang2024mrse:,
    title={MRSE: An Efficient Multi-modality Retrieval System for Large Scale
  E-commerce},
    author={Hao Jiang, Haoxiang Zhang, Qingshan Hou, Chaofeng Chen, Weisi Lin,
  Jingchang Zhang, Annan Wang},
    journal={arXiv preprint arXiv:2408.14968},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14968},
    primaryClass={cs.IR cs.CL}
}