10 citations · 63 across the 40 of their papers we have counts for
13 papers · 1 filter
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…
Watermarking Recommender Systems
Sixiao Zhang, Cheng Long, Wei Yuan +2
Recommender systems embody significant commercial value and represent crucial intellectual property. However, the integrity of these systems is constantly challenged by malicious a…
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…
Poisoning Attacks and Defenses in Recommender Systems: A Survey
Zongwei Wang, Junliang Yu, Min Gao +4
Modern recommender systems (RS) have profoundly enhanced user experience across digital platforms, yet they face significant threats from poisoning attacks. These attacks, aimed at…
Automated Similarity Metric Generation for Recommendation
Liang Qu, Yun Lin, Wei Yuan +3
The embedding-based architecture has become the dominant approach in modern recommender systems, mapping users and items into a compact vector space. It then employs predefined sim…
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…
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
Liang Qu, Wei Yuan, Ruiqi Zheng +3
Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating…
ROIC-DM: Robust Text Inference and Classification via Diffusion Model
Shilong Yuan, Wei Yuan, Hongzhi Yin +1
While language models have made many milestones in text inference and classification tasks, they remain susceptible to adversarial attacks that can lead to unforeseen outcomes. Exi…
On-Device Recommender Systems: A Comprehensive Survey
Hongzhi Yin, Liang Qu, Tong Chen +6
Recommender systems have been widely deployed in various real-world applications to help users identify content of interest from massive amounts of information. Traditional recomme…