---
license: apache-2.0
task_categories:
- text-classification
- text-generation
- image-classification
- image-to-text
language:
- zh
- en
size_categories:
- 1M<n<10M
---

This is the HuggingFace repository of the paper named [MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding](https://arxiv.org/abs/2508.11999v5) 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}
}
```