Daoze/MM-Bench-E-Commerce

This is the HuggingFace repository of the paper named MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding in WSDM 2026 (oral).

In this paper, we argue that generative Multimodal Large Language Models (MLLMs) hold significant potential for improving product representation learning. We propose the first generative MLLM-based model named MOON for product representation learning.

Furthermore, we contruct and publish a large-scale real-world multimodal benchmark named MM-Bench-E-Commerce(MBE) for product understanding, which supports a wide range of downstream tasks, including various cross-modal retrieval, multi-granularity product classification, attribute prediction and so on. Our benchmark comprises 2.7M training samples and 410k evaluation samples, all collected from real-world products and user purchases on Taobao, one of the largest e-commerce platforms in China. The retrieval tasks involved are grounded in actual purchase behaviors rather than trivial category matching, thereby offering a more realistic assessment of the product understanding ability in practical applications.

@article{zhang2025moon,
  title={MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding},
  author={Zhang, Daoze and Fu, Chenghan and Nie, Zhanheng and Liu, Jianyu and Guan, Wanxian and Gao, Yuan and Song, Jun and Wang, Pengjie and Xu, Jian and Zheng, Bo},
  journal={arXiv preprint arXiv:2508.11999},
  year={2025}
}