Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Ye, Guanhua"'
Decentralized learning has become crucial for collaborative model training in environments where data privacy and trust are paramount. In web-based applications, clients are liberated from traditional fixed network topologies, enabling the establishm
Externí odkaz:
http://arxiv.org/abs/2410.11378
Decentralized collaborative learning for Point-of-Interest (POI) recommendation has gained research interest due to its advantages in privacy preservation and efficiency, as it keeps data locally and leverages collaborative learning among clients to
Externí odkaz:
http://arxiv.org/abs/2410.10130
Federated sequential recommendation (FedSeqRec) has gained growing attention due to its ability to protect user privacy. Unfortunately, the performance of FedSeqRec is still unsatisfactory because the models used in FedSeqRec have to be lightweight t
Externí odkaz:
http://arxiv.org/abs/2410.04927
Urban flow prediction is a spatio-temporal modeling task that estimates the throughput of transportation services like buses, taxis, and ride-sharing, where data-driven models have become the most popular solution in the past decade. Meanwhile, the i
Externí odkaz:
http://arxiv.org/abs/2407.13605
Graph neural networks (GNNs) have attracted widespread attention for their impressive capability of graph representation learning. However, the increasing prevalence of large-scale graphs presents a significant challenge for GNN training due to their
Externí odkaz:
http://arxiv.org/abs/2406.13200
Sequential recommender systems have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning for sequential recommendation, a.k.a., Federated Sequential Recommender
Externí odkaz:
http://arxiv.org/abs/2406.05387
Modern recommender systems (RS) have profoundly enhanced user experience across digital platforms, yet they face significant threats from poisoning attacks. These attacks, aimed at manipulating recommendation outputs for unethical gains, exploit vuln
Externí odkaz:
http://arxiv.org/abs/2406.01022
The rapid expansion of Location-Based Social Networks (LBSNs) has highlighted the importance of effective next Point-of-Interest (POI) recommendations, which leverage historical check-in data to predict users' next POIs to visit. Traditional centrali
Externí odkaz:
http://arxiv.org/abs/2405.13811
Autor:
Gao, Xinyi, Chen, Tong, Zhang, Wentao, Yu, Junliang, Ye, Guanhua, Nguyen, Quoc Viet Hung, Yin, Hongzhi
The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements. In response, graph condensation (GC) emerges as a promising data-centric solu
Externí odkaz:
http://arxiv.org/abs/2405.13707
Federated Recommender Systems (FedRecs) have garnered increasing attention recently, thanks to their privacy-preserving benefits. However, the decentralized and open characteristics of current FedRecs present two dilemmas. First, the performance of F
Externí odkaz:
http://arxiv.org/abs/2403.20107