Create README.md

README.md

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+---
+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/pdf/2508.11999) 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}
+}
+```