activity
20192026
collaborators

9 papers

cs.CV2026

PailitaoGR: Latent Think-with-Images for Generative Image Retrieval

Xiaomeng Fan, Yueran Liu, Shengyu Zhou +6

Generative retrieval has demonstrated strong performance by directly generating product semantic identifiers (SIDs). Extending this paradigm to image search, however, is nontrivial…

cs.LG2026

MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding

Junxian Wu, Chenghan Fu, Zhanheng Nie +6

With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention. Although recent multimodal large language…

cs.IR2025

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

Chenghan Fu, Daoze Zhang, Yukang Lin +8

We introduce MOON, our comprehensive set of sustainable iterative practices for multimodal representation learning for e-commerce applications. MOON has already been fully deployed…

cs.CV2025

MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding

Zhanheng Nie, Chenghan Fu, Daoze Zhang +5

Recent Multimodal Large Language Models (MLLMs) have significantly advanced e-commerce product understanding. However, they still face three challenges: (i) the modality imbalance…

cs.CV2025

Creative4U: MLLMs-based Advertising Creative Image Selector with Comparative Reasoning

Yukang Lin, Xiang Zhang, Shichang Jia +9

Creative image in advertising is the heart and soul of e-commerce platform. An eye-catching creative image can enhance the shopping experience for users, boosting income for advert…

cs.CV2025

MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding

Daoze Zhang, Chenghan Fu, Zhanheng Nie +7

With the rapid advancement of e-commerce, exploring general representations rather than task-specific ones has attracted increasing research attention. For product understanding, a…

cs.IR2025

MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling

Bencheng Yan, Si Chen, Shichang Jia +12

Click-Through Rate (CTR) prediction is a crucial task in recommendation systems, online searches, and advertising platforms, where accurately capturing users' real interests in con…

cs.IR2022

Visual Encoding and Debiasing for CTR Prediction

Si Chen, Chen Lin, Wanxian Guan +7

Extracting expressive visual features is crucial for accurate Click-Through-Rate (CTR) prediction in visual search advertising systems. Current commercial systems use off-the-shelf…

cs.IR2019

Personalized Attraction Enhanced Sponsored Search with Multi-task Learning

Wei Zhao, Boxuan Zhang, Beidou Wang +6

We study a novel problem of sponsored search (SS) for E-Commerce platforms: how we can attract query users to click product advertisements (ads) by presenting them features of prod…