1 citations · 2 across the 8 of their papers we have counts for
12 papers
UniDGF: A Unified Detection-to-Generation Framework for Hierarchical Object Visual Recognition
Xinyu Nan, Lingtao Mao, Huangyu Dai +8
Achieving visual semantic understanding requires a unified framework that simultaneously handles object detection, category prediction, and attribute recognition. However, current…
OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search
Ben Chen, Xian Guo, Siyuan Wang +25
Traditional e-commerce search systems employ multi-stage cascading architectures (MCA) that progressively filter items through recall, pre-ranking, and ranking stages. While effect…
GRADE: Personalized Multi-Task Fusion via Group-relative Reinforcement Learning with Adaptive Dirichlet Exploration
Tingfeng Hong, Pingye Ren, Xinlong Xiao +4
Balancing multiple objectives is critical for user satisfaction in modern recommender and search systems, yet current Multi-Task Fusion (MTF) methods rely on static, manually-tuned…
COINS: SemantiC Ids Enhanced COLd Item RepresentatioN for Click-through Rate Prediction in E-commerce Search
Qihang Zhao, Zhongbo Sun, Xiaoyang Zheng +6
With the rise of modern search and recommendation platforms, insufficient collaborative information of cold-start items exacerbates the Matthew effect of existing platform items, c…
OneVision: An End-to-End Generative Framework for Multi-view E-commerce Vision Search
Zexin Zheng, Huangyu Dai, Lingtao Mao +8
Traditional vision search, similar to search and recommendation systems, follows the multi-stage cascading architecture (MCA) paradigm to balance efficiency and conversion. Specifi…
Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
Qihang Zhao, Xiaoyang Zheng, Ben Chen +2
User behavior sequences in search systems resemble "interest fossils", capturing genuine intent yet eroded by exposure bias, category drift, and contextual noise. Current methods p…