6 papers
You Only Anonymize What Is Not Intent-Relevant: Suppressing Non-Intent Privacy Evidence
Weihao Shen, Yaxin Xu, Shuang Li +4
Anonymizing sensitive information in user text is essential for privacy, yet existing methods often apply uniform treatment across attributes, which can conflict with communicative…
DUET: Dual Model Co-Training for Entire Space CTR Prediction
Yutian Xiao, Meng Yuan, Fuzhen Zhuang +9
The pre-ranking stage plays a pivotal role in large-scale recommender systems but faces an intrinsic trade-off between model expressiveness and computational efficiency. Owing to t…
FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis
Wei Chen, Zhao Zhang, Meng Yuan +2
In this paper, we address the task of targeted sentiment analysis (TSA), which involves two sub-tasks, i.e., identifying specific aspects from reviews and determining their corresp…
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…
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…
Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
Wei Chen, Guo Ye, Yakun Wang +5
Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graph…