3 citations · 4 across the 14 of their papers we have counts for
14 papers
Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering
Jin-Duk Park, Won-Yong Shin
Graph convolutional networks (GCNs) have demonstrated significant success in capturing complex user-item relationships for collaborative filtering (CF). However, due to their relia…
Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation
Yu-Seung Roh, Joo-Young Kim, Jin-Duk Park +1
Multimodal recommender systems improve the performance of canonical recommender systems with no item features by utilizing diverse content types such as text, images, and videos, w…
Leveraging Member-Group Relations via Multi-View Graph Filtering for Effective Group Recommendation
Chae-Hyun Kim, Yoon-Ryung Choi, Jin-Duk Park +1
Group recommendation aims at providing optimized recommendations tailored to diverse groups, enabling groups to enjoy appropriate items. On the other hand, most existing group reco…
Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation
Jin-Duk Park, Jaemin Yoo, Won-Yong Shin
Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommen…
CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems
Jin-Duk Park, Kyung-Min Kim, Won-Yong Shin
Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations. Meanwhile, a number of CF techniq…
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs
Siqing Li, Jin-Duk Park, Wei Huang +3
Heterogeneous graph neural networks (HGNNs) have significantly propelled the information retrieval (IR) field. Still, the effectiveness of HGNNs heavily relies on high-quality labe…
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…
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…
Is Generative Modeling-based Stylization Necessary for Domain Adaptation in Regression Tasks?
Jinman Park, Francois Barnard, Saad Hossain +2
Unsupervised domain adaptation (UDA) aims to bridge the gap between source and target domains in the absence of target domain labels using two main techniques: input-level alignmen…
Centrality-Based Node Feature Augmentation for Robust Network Alignment
Jin-Duk Park, Cong Tran, Won-Yong Shin +1
Network alignment (NA) is the task of discovering node correspondences across multiple networks. Although NA methods have achieved remarkable success in a myriad of scenarios, thei…
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…
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data
Kyeong-Joong Jeong, Jin-Duk Park, Kyusoon Hwang +2
We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line. Given heterogeneous time series data consisting of oper…
On the Power of Gradual Network Alignment Using Dual-Perception Similarities
Jin-Duk Park, Cong Tran, Won-Yong Shin +1
Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fac…