MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising

Fuente: arXiv

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Bibliographic Details
Main Authors:	Fu, Chenghan, Zhang, Daoze, Lin, Yukang, Nie, Zhanheng, Zhang, Xiang, Liu, Jianyu, Liu, Yueran, Guan, Wanxian, Wang, Pengjie, Xu, Jian, Zheng, Bo
Format:	Preprint
Published:	2025
Subjects:	Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
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_version_	1866911273141141504
author	Fu, Chenghan Zhang, Daoze Lin, Yukang Nie, Zhanheng Zhang, Xiang Liu, Jianyu Liu, Yueran Guan, Wanxian Wang, Pengjie Xu, Jian Zheng, Bo
author_facet	Fu, Chenghan Zhang, Daoze Lin, Yukang Nie, Zhanheng Zhang, Xiang Liu, Jianyu Liu, Yueran Guan, Wanxian Wang, Pengjie Xu, Jian Zheng, Bo
contents	We introduce MOON, our comprehensive set of sustainable iterative practices for multimodal representation learning for e-commerce applications. MOON has already been fully deployed across all stages of Taobao search advertising system, including retrieval, relevance, ranking, and so on. The performance gains are particularly significant on click-through rate (CTR) prediction task, which achieves an overall +20.00% online CTR improvement. Over the past three years, this project has delivered the largest improvement on CTR prediction task and undergone five full-scale iterations. Throughout the exploration and iteration of our MOON, we have accumulated valuable insights and practical experience that we believe will benefit the research community. MOON contains a three-stage training paradigm of "Pretraining, Post-training, and Application", allowing effective integration of multimodal representations with downstream tasks. Notably, to bridge the misalignment between the objectives of multimodal representation learning and downstream training, we define the exchange rate to quantify how effectively improvements in an intermediate metric can translate into downstream gains. Through this analysis, we identify the image-based search recall as a critical intermediate metric guiding the optimization of multimodal models. Over three years and five iterations, MOON has evolved along four critical dimensions: data processing, training strategy, model architecture, and downstream application. The lessons and insights gained through the iterative improvements will also be shared. As part of our exploration into scaling effects in the e-commerce field, we further conduct a systematic study of the scaling laws governing multimodal representation learning, examining multiple factors such as the number of training tokens, negative samples, and the length of user behavior sequences.
format	Preprint
id	arxiv_https___arxiv_org_abs_2511_11305
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising Fu, Chenghan Zhang, Daoze Lin, Yukang Nie, Zhanheng Zhang, Xiang Liu, Jianyu Liu, Yueran Guan, Wanxian Wang, Pengjie Xu, Jian Zheng, Bo Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning We introduce MOON, our comprehensive set of sustainable iterative practices for multimodal representation learning for e-commerce applications. MOON has already been fully deployed across all stages of Taobao search advertising system, including retrieval, relevance, ranking, and so on. The performance gains are particularly significant on click-through rate (CTR) prediction task, which achieves an overall +20.00% online CTR improvement. Over the past three years, this project has delivered the largest improvement on CTR prediction task and undergone five full-scale iterations. Throughout the exploration and iteration of our MOON, we have accumulated valuable insights and practical experience that we believe will benefit the research community. MOON contains a three-stage training paradigm of "Pretraining, Post-training, and Application", allowing effective integration of multimodal representations with downstream tasks. Notably, to bridge the misalignment between the objectives of multimodal representation learning and downstream training, we define the exchange rate to quantify how effectively improvements in an intermediate metric can translate into downstream gains. Through this analysis, we identify the image-based search recall as a critical intermediate metric guiding the optimization of multimodal models. Over three years and five iterations, MOON has evolved along four critical dimensions: data processing, training strategy, model architecture, and downstream application. The lessons and insights gained through the iterative improvements will also be shared. As part of our exploration into scaling effects in the e-commerce field, we further conduct a systematic study of the scaling laws governing multimodal representation learning, examining multiple factors such as the number of training tokens, negative samples, and the length of user behavior sequences.
title	MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising
topic	Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning
url	https://arxiv.org/abs/2511.11305