9 citations · 24 across the 11 of their papers we have counts for
11 papers
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…
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…
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System
Wei Yuan, Chaoqun Yang, Liang Qu +3
Sequential recommender systems have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning fo…
Robust Federated Contrastive Recommender System against Model Poisoning Attack
Wei Yuan, Chaoqun Yang, Liang Qu +3
Federated Recommender Systems (FedRecs) have garnered increasing attention recently, thanks to their privacy-preserving benefits. However, the decentralized and open characteristic…
Hide Your Model: A Parameter Transmission-free Federated Recommender System
Wei Yuan, Chaoqun Yang, Liang Qu +3
With the growing concerns regarding user data privacy, Federated Recommender System (FedRec) has garnered significant attention recently due to its privacy-preserving capabilities.…
Motif-Based Prompt Learning for Universal Cross-Domain Recommendation
Bowen Hao, Chaoqun Yang, Lei Guo +2
Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target d…
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures
Wei Yuan, Shilong Yuan, Chaoqun Yang +2
Federated recommender systems (FedRecs) have been widely explored recently due to their ability to protect user data privacy. In FedRecs, a central server collaboratively learns re…
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling
Xuhui Ren, Wei Yuan, Tong Chen +3
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences. Despite recent advances in KG-ba…
Interaction-level Membership Inference Attack Against Federated Recommender Systems
Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen +3
The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In Fed…
Efficient On-Device Session-Based Recommendation
Xin Xia, Junliang Yu, Qinyong Wang +3
On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing pro…