3 citations · 4 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…