Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Yu, Junliang"'
Autor:
Zhang, Qianru, Yang, Peng, Yu, Junliang, Wang, Haixin, He, Xingwei, Yiu, Siu-Ming, Yin, Hongzhi
The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Point-of-Interest (POI) recommendation systems. These advanced POI system
Externí odkaz:
http://arxiv.org/abs/2410.02191
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
Tripartite graph-based recommender systems markedly diverge from traditional models by recommending unique combinations such as user groups and item bundles. Despite their effectiveness, these systems exacerbate the longstanding cold-start problem in
Externí odkaz:
http://arxiv.org/abs/2407.05126
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
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
Cross-domain Recommendation (CDR) as one of the effective techniques in alleviating the data sparsity issues has been widely studied in recent years. However, previous works may cause domain privacy leakage since they necessitate the aggregation of d
Externí odkaz:
http://arxiv.org/abs/2401.14678
The rapid growth of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs). To address these challenges, graph condensation (GC) has emerged as an innovative solution. GC focuse
Externí odkaz:
http://arxiv.org/abs/2401.11720
Modern recommender systems (RS) have seen substantial success, yet they remain vulnerable to malicious activities, notably poisoning attacks. These attacks involve injecting malicious data into the training datasets of RS, thereby compromising their
Externí odkaz:
http://arxiv.org/abs/2401.01527
Contrastive learning (CL) has recently gained prominence in the domain of recommender systems due to its great ability to enhance recommendation accuracy and improve model robustness. Despite its advantages, this paper identifies a vulnerability of C
Externí odkaz:
http://arxiv.org/abs/2311.18244
Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target domain. However, existing CDR models suffer limitations in adaptability
Externí odkaz:
http://arxiv.org/abs/2310.13303