Zobrazeno 1 - 10
of 181
pro vyhledávání: '"Nguyễn Quốc Việt"'
Autor:
Jiang, Haiyang, Chen, Tong, Zhang, Wentao, Hung, Nguyen Quoc Viet, Yuan, Yuan, Li, Yong, Cui, Lizhen
Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Temporal Graph Neural Networks (STGNNs) have established themselves as ca
Externí odkaz:
http://arxiv.org/abs/2412.05534
Among various spatio-temporal prediction tasks, epidemic forecasting plays a critical role in public health management. Recent studies have demonstrated the strong potential of spatio-temporal graph neural networks (STGNNs) in extracting heterogeneou
Externí odkaz:
http://arxiv.org/abs/2411.17372
Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Although substantial progress has been made in time series forecastin
Externí odkaz:
http://arxiv.org/abs/2411.15716
Group Point-of-Interest (POI) recommendations aim to predict the next POI that satisfies the diverse preferences of a group of users. This task is more challenging than traditional individual POI recommendations due to complex group decision-making a
Externí odkaz:
http://arxiv.org/abs/2411.13415
Content-based Recommender Systems (CRSs) play a crucial role in shaping user experiences in e-commerce, online advertising, and personalized recommendations. However, due to the vast amount of categorical features, the embedding tables used in CRS mo
Externí odkaz:
http://arxiv.org/abs/2411.13052
Hyperedge prediction is crucial in hypergraph analysis for understanding complex multi-entity interactions in various web-based applications, including social networks and e-commerce systems. Traditional methods often face difficulties in generating
Externí odkaz:
http://arxiv.org/abs/2411.12354
Autor:
Nguyen, Thanh Tam, Ren, Zhao, Pham, Trinh, Huynh, Thanh Trung, Nguyen, Phi Le, Yin, Hongzhi, Nguyen, Quoc Viet Hung
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides in instruc
Externí odkaz:
http://arxiv.org/abs/2411.09955
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
Autor:
Wang, Zongwei, Gao, Min, Yu, Junliang, Gao, Xinyi, Nguyen, Quoc Viet Hung, Sadiq, Shazia, Yin, Hongzhi
The ID-free recommendation paradigm has been proposed to address the limitation that traditional recommender systems struggle to model cold-start users or items with new IDs. Despite its effectiveness, this study uncovers that ID-free recommender sys
Externí odkaz:
http://arxiv.org/abs/2409.11690
As a branch of advanced artificial intelligence, dialogue systems are prospering. Multi-turn response selection is a general research problem in dialogue systems. With the assistance of background information and pre-trained language models, the perf
Externí odkaz:
http://arxiv.org/abs/2407.18479