14 papers
Residual Trajectory Distillation for Generative Retrieval
Weihao Shen, Wei Chen, Fuwei Zhang +4
Generative retrieval has emerged as a general retrieval paradigm, representing items with discrete Semantic IDs (SIDs) and retrieving them through autoregressive identifier generat…
Learning Multiresolution Relevance for Hierarchical Generative Retrieval
Weihao Shen, Wei Chen, Fuwei Zhang +4
Generative retrieval with semantic identifiers (SIDs) makes successive decisions over a document hierarchy. Relevant documents for the same query may share coarse prefixes and dive…
Requirement--Evidence Alignment for Compositional E-Commerce Queries
Weihao Shen, Wei Chen, Fuwei Zhang +6
Compositional e-commerce queries express multiple requirements that must hold jointly, yet existing rerankers collapse these constraints into aggregate relevance and often promote…
Unpaired Modality-Agnostic Generative Recommendation
Weihao Shen, Wei Chen, Fuwei Zhang +6
Generative Recommendation (GR) formulates recommendation as autoregressive generation over discrete semantic identifiers (IDs). Although recent multimodal GR methods improve semant…
SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
Wei Chen, Xingyu Guo, Shuang Li +6
Generative Recommendation (GR) has emerged as a promising paradigm by formulating item recommendation as a sequence-to-sequence generation task over item identifiers. Recent studie…
Learning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptation
Wei Chen, Xingyu Guo, Shuang Li +4
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs but is challenged by complex, multi-faceted distributional shifts. Existing…
Learning Structure-Semantic Evolution Trajectories for Graph Domain Adaptation
Wei Chen, Xingyu Guo, Shuang Li +7
Graph Domain Adaptation (GDA) aims to bridge distribution shifts between domains by transferring knowledge from well-labeled source graphs to given unlabeled target graphs. One pro…
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…
FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning
Wei Chen, Meng Yuan, Zhao Zhang +4
As trustworthy AI continues to advance, the fairness issue in recommendations has received increasing attention. A recommender system is considered unfair when it produces unequal…