8 citations · 11 across the 12 of their papers we have counts for
11 papers
Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
Jin-Duk Park, Yong-Min Shin, Won-Yong Shin
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…
Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
Yu Hou, Jin-Duk Park, Won-Yong Shin
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. H…
Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching
Sujin Kook, Won-Yong Shin, Seong-Lyun Kim +1
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…
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs
Yong-Min Shin, Won-Yong Shin
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) f…
Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
Jin-Duk Park, Siqing Li, Xin Cao +1
The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural n…
Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation
Jin-Duk Park, Cong Tran, Won-Yong Shin +1
Network alignment (NA) is the task of discovering node correspondences across different networks. Although NA methods have achieved remarkable success in a myriad of scenarios, the…