Zobrazeno 1 - 10
of 289
pro vyhledávání: '"NGUYEN QUOC VIET HUNG"'
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
Autor:
Van-Hau Nguyen, Tran Thi Tuyet-Hanh, James Mulhall, Hoang Van Minh, Trung Q Duong, Nguyen Van Chien, Nguyen Thi Trang Nhung, Vu Hoang Lan, Hoang Ba Minh, Do Cuong, Nguyen Ngoc Bich, Nguyen Huu Quyen, Tran Nu Quy Linh, Nguyen Thi Tho, Ngu Duy Nghia, Le Van Quoc Anh, Diep T M Phan, Nguyen Quoc Viet Hung, Mai Thai Son
Publikováno v:
PLoS Neglected Tropical Diseases, Vol 16, Iss 6, p e0010509 (2022)
BackgroundDengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate
Externí odkaz:
https://doaj.org/article/da592396b0394e08ae736e8c42450672
Recommender systems typically represent users and items by learning their embeddings, which are usually set to uniform dimensions and dominate the model parameters. However, real-world recommender systems often operate in streaming recommendation sce
Externí odkaz:
http://arxiv.org/abs/2407.15411
Autor:
Sakong, Darnbi, Vu, Viet Hung, Huynh, Thanh Trung, Nguyen, Phi Le, Yin, Hongzhi, Nguyen, Quoc Viet Hung, Nguyen, Thanh Tam
Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendat
Externí odkaz:
http://arxiv.org/abs/2407.03665
Since the creation of the Web, recommender systems (RSs) have been an indispensable mechanism in information filtering. State-of-the-art RSs primarily depend on categorical features, which ecoded by embedding vectors, resulting in excessively large e
Externí odkaz:
http://arxiv.org/abs/2406.17335