32 citations · 58 across the 37 of their papers we have counts for
6 papers · 1 filter
One Sequential Recommendation Model Pretrained from Synthetic Priors Predicts Multiple Datasets
Woosung Kang, Jiwon Jeong, Jonghyeok Shin +2
Existing sequential recommendation models rely on dataset-specific training, where the learned parameters are fitted to the item catalog and the observed interaction distribution o…
TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
Yehjin Shin, Jeongwhan Choi, Seojin Kim +1
Recently, convolutional filters have been increasingly adopted in sequential recommendation for their ability to capture local sequential patterns. However, most of these models co…
Towards Unified and Adaptive Cross-Domain Collaborative Filtering via Graph Signal Processing
Jeongeun Lee, Seongku Kang, Won-Yong Shin +3
Collaborative Filtering (CF) is a foundational approach in recommender systems, but it struggles with challenges such as data sparsity and the cold-start problem. Cross-Domain Reco…
SVD-AE: Simple Autoencoders for Collaborative Filtering
Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee +2
Collaborative filtering (CF) methods for recommendation systems have been extensively researched, ranging from matrix factorization and autoencoder-based to graph filtering-based m…
SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation
Chaejeong Lee, Jeongwhan Choi, Hyowon Wi +2
Graph-based collaborative filtering (CF) has emerged as a promising approach in recommender systems. Despite its achievements, graph-based CF models face challenges due to data spa…
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
Jeongwhan Choi, Hyowon Wi, Chaejeong Lee +3
Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by using self-supervised signals from ra…