Zobrazeno 1 - 10
of 276
pro vyhledávání: '"Nguyen, Quoc Viet Hung"'
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
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
Autor:
Huynh, Thanh Trung, Nguyen, Trong Bang, Nguyen, Phi Le, Nguyen, Thanh Tam, Weidlich, Matthias, Nguyen, Quoc Viet Hung, Aberer, Karl
Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data. The increasing demand to address issues such as ``the right to be forgotten'' and combat data poisoning
Externí odkaz:
http://arxiv.org/abs/2405.18040
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
Autor:
Nguyen, Thanh Toan, Nguyen, Quoc Viet Hung, Nguyen, Thanh Tam, Huynh, Thanh Trung, Nguyen, Thanh Thi, Weidlich, Matthias, Yin, Hongzhi
Recommender systems have become an integral part of online services to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks, particularly those that
Externí odkaz:
http://arxiv.org/abs/2404.14942
Autor:
Nguyen, Thanh Tam, Huynh, Thanh Trung, Ren, Zhao, Nguyen, Thanh Toan, Nguyen, Phi Le, Yin, Hongzhi, Nguyen, Quoc Viet Hung
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model exp
Externí odkaz:
http://arxiv.org/abs/2404.00673
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
Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences. While the incorporation of visual information holds the promise of enhanc
Externí odkaz:
http://arxiv.org/abs/2312.15826