10 citations · 63 across the 40 of their papers we have counts for
44 papers
VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
Shutong Qiao, Wei Yuan, Tong Chen +3
Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization. However, these artificial codes…
Self-Gating Attention for Efficient Time Series Forecasting
Dezheng Wang, Tong Chen, Wei Yuan +3
Transformer architectures have shown strong potential in time series forecasting, where multi-head self-attention is widely used to capture temporal dependencies across historical…
Federated Learning and Unlearning for Recommendation with Personalized Data Sharing
Liang Qu, Jianxin Li, Wei Yuan +4
Federated recommender systems (FedRS) have emerged as a paradigm for protecting user privacy by keeping interaction data on local devices while coordinating model training through…
Agentic Mixed-Source Multi-Modal Misinformation Detection with Adaptive Test-Time Scaling
Wei Jiang, Tong Chen, Wei Yuan +2
Vision-language models (VLMs) have been proven effective for detecting multi-modal misinformation on social platforms, especially in zero-shot settings with unavailable or delayed…
SDFed: Bridging Local Global Discrepancy via Subspace Refinement and Divergence Control in Federated Prompt Learning
Yicheng Di, Wei Yuan, Tieke He +2
Vision-language pretrained models offer strong transferable representations, yet adapting them in privacy-sensitive multi-party settings is challenging due to the high communicatio…
When Text-as-Vision Meets Semantic IDs in Generative Recommendation: An Empirical Study
Shutong Qiao, Wei Yuan, Tong Chen +3
Semantic ID learning is a key interface in Generative Recommendation (GR) models, mapping items to discrete identifiers grounded in side information, most commonly via a pretrained…
Integrating Vision-Centric Text Understanding for Conversational Recommender Systems
Wei Yuan, Shutong Qiao, Tong Chen +3
Conversational Recommender Systems (CRSs) have attracted growing attention for their ability to deliver personalized recommendations through natural language interactions. To more…
QA-Merging: Query-Adaptive Reasoning via Layer Selective Model Merging
Zhaofeng Zhong, Wei Yuan, Tong Chen +4
Recent large reasoning models (LRMs) have achieved strong performance on complex reasoning tasks by generating a long chain-of-thought (Long-CoT). However, such lengthy reasoning i…
TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding
Xiaobo Xing, Wei Yuan, Tong Chen +3
Modeling semantic and structural information from tabular data remains a core challenge for effective table understanding. Existing Table-as-Text approaches flatten tables for larg…
Towards Propagation-aware Representation Learning for Supervised Social Media Graph Analytics
Wei Jiang, Tong Chen, Wei Yuan +3
Social media platforms generate vast, complex graph-structured data, facilitating diverse tasks such as rumor detection, bot identification, and influence modeling. Real-world appl…
Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation
Liang Qu, Jianxin Li, Wei Yuan +3
Federated recommender systems have emerged as a promising privacy-preserving paradigm, enabling personalized recommendation services without exposing users' raw data. By keeping da…
Controllable and Stealthy Shilling Attacks via Dispersive Latent Diffusion
Shutong Qiao, Wei Yuan, Junliang Yu +3
Recommender systems (RSs) are now fundamental to various online platforms, but their dependence on user-contributed data leaves them vulnerable to shilling attacks that can manipul…
A Provably Secure Network Protocol for Private Communication with Analysis and Tracing Resistance
Chao Ge, Wei Yuan, Ge Chen +2
Anonymous communication networks have emerged as crucial tools for obfuscating communication pathways and concealing user identities. However, their practical deployments face sign…
Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model
Zhaofeng Zhong, Wei Yuan, Liang Qu +4
With the advancement of large language models (LLMs), significant progress has been achieved in various Natural Language Processing (NLP) tasks. However, existing LLMs still face t…
Watermarking Large Language Model-based Time Series Forecasting
Wei Yuan, Chaoqun Yang, Yu Xing +3
Large Language Model-based Time Series Forecasting (LLMTS) has shown remarkable promise in handling complex and diverse temporal data, representing a significant step toward founda…
On Mitigating Data Sparsity in Conversational Recommender Systems
Sixiao Zhang, Mingrui Liu, Wei Yuan +2
Conversational recommender systems (CRSs) infer user preferences from dialogue contexts, but they suffer from severe data sparsity in both dialogue and entity spaces. Dialogue data…
Lightweight Embeddings with Graph Rewiring for Collaborative Filtering
Xurong Liang, Tong Chen, Wei Yuan +1
As recommendation services scale rapidly and their deployment now commonly involves resource-constrained edge devices, GNN-based recommender systems face significant challenges, in…
HMamba: Hyperbolic Mamba for Sequential Recommendation
Qianru Zhang, Honggang Wen, Wei Yuan +4
Sequential recommendation systems have become a cornerstone of personalized services, adept at modeling the temporal evolution of user preferences by capturing dynamic interaction…
Multi-agents based User Values Mining for Recommendation
Lijian Chen, Wei Yuan, Tong Chen +3
Recommender systems have rapidly evolved and become integral to many online services. However, existing systems sometimes produce unstable and unsatisfactory recommendations that f…
Tackling Data Heterogeneity in Federated Time Series Forecasting
Wei Yuan, Guanhua Ye, Xiangyu Zhao +3
Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting.…
Data Watermarking for Sequential Recommender Systems
Sixiao Zhang, Cheng Long, Wei Yuan +2
In the era of large foundation models, data has become a crucial component in building high-performance AI systems. As the demand for high-quality and large-scale data continues to…
LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation
Shutong Qiao, Chen Gao, Wei Yuan +2
Sequential recommendation (SR) leverages users' dynamic preferences, with recent advances incorporating multi-interest learning to model diverse user interests. However, most multi…
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services
Wei Yuan, Chaoqun Yang, Guanhua Ye +3
Federated sequential recommendation (FedSeqRec) has gained growing attention due to its ability to protect user privacy. Unfortunately, the performance of FedSeqRec is still unsati…
PDC-FRS: Privacy-preserving Data Contribution for Federated Recommender System
Chaoqun Yang, Wei Yuan, Liang Qu +1
Federated recommender systems (FedRecs) have emerged as a popular research direction for protecting users' privacy in on-device recommendations. In FedRecs, users keep their data l…