Zobrazeno 1 - 10
of 495
pro vyhledávání: '"Shin, Won Yong"'
Heterogeneous graph neural networks (HGNNs) have significantly propelled the information retrieval (IR) field. Still, the effectiveness of HGNNs heavily relies on high-quality labels, which are often expensive to acquire. This challenge has shifted a
Externí odkaz:
http://arxiv.org/abs/2409.06323
Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations. Meanwhile, a number of CF techniques have evolved into sophisticated model architectures based on multi
Externí odkaz:
http://arxiv.org/abs/2409.05878
Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem. While CDR offers substantial potential for enhancing recommendati
Externí odkaz:
http://arxiv.org/abs/2407.12374
Autor:
Shin, Yong-Min, Shin, Won-Yong
As one of popular quantitative metrics to assess the quality of explanation of graph neural networks (GNNs), fidelity measures the output difference after removing unimportant parts of the input graph. Fidelity has been widely used due to its straigh
Externí odkaz:
http://arxiv.org/abs/2406.11504
The self-attention mechanism has been adopted in various popular message passing neural networks (MPNNs), enabling the model to adaptively control the amount of information that flows along the edges of the underlying graph. Such attention-based MPNN
Externí odkaz:
http://arxiv.org/abs/2406.04612
A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a training process. However, conventional GF-based CF approaches mostly
Externí odkaz:
http://arxiv.org/abs/2404.14243
A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. However, existing diffusion model-based recommender systems do not expl
Externí odkaz:
http://arxiv.org/abs/2404.14240
The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server
Externí odkaz:
http://arxiv.org/abs/2402.11925
In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively
Externí odkaz:
http://arxiv.org/abs/2312.09511
Autor:
Shin, Yong-Min, Shin, Won-Yong
Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semi-supervised node classification on graphs, by training a student MLP by knowledge distillation (KD) from a teacher graph neural network (GNN). While previous studies have
Externí odkaz:
http://arxiv.org/abs/2311.11759