activity
20242026
most citedYour Group-Relative Advantage Is Biased

1 citations · 1 across the 7 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.IR2025

FLeW: Facet-Level and Adaptive Weighted Representation Learning of Scientific Documents

Zheng Dou, Deqing Wang, Fuzhen Zhuang +2

Scientific document representation learning provides powerful embeddings for various tasks, while current methods face challenges across three approaches. 1) Contrastive training w…

cs.IR2025

ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling

Huishi Luo, Fuzhen Zhuang, Yongchun Zhu +6

Dwell time (DT) is a critical post-click metric for evaluating user preference in recommender systems, complementing the traditional click-through rate (CTR). Although multi-task l…

cs.IR2025

CDC: Causal Domain Clustering for Multi-Domain Recommendation

Huishi Luo, Yiqing Wu, Yiwen Chen +2

Multi-domain recommendation leverages domain-general knowledge to improve recommendations across several domains. However, as platforms expand to dozens or hundreds of scenarios, t…

cs.CL2025

AgriCHN: A Comprehensive Cross-domain Resource for Chinese Agricultural Named Entity Recognition

Lingxiao Zeng, Yiqi Tong, Wei Guo +6

Agricultural named entity recognition is a specialized task focusing on identifying distinct agricultural entities within vast bodies of text, including crops, diseases, pests, and…

cs.IR2025

Hyperbolic Diffusion Recommender Model

Meng Yuan, Yutian Xiao, Wei Chen +3

Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender sy…

cs.AI2025

Bridging Social Psychology and LLM Reasoning: Conflict-Aware Meta-Review Generation via Cognitive Alignment

Wei Chen, Han Ding, Meng Yuan +3

The rapid growth of scholarly submissions has overwhelmed traditional peer review systems, driving the need for intelligent automation to preserve scientific rigor. While large lan…