550 citations · 718 across the 45 of their papers we have counts for
4 papers · 1 filter
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Seoyoung Hong, Minju Jo, Seungji Kook +4
Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-worl…
Blurring-Sharpening Process Models for Collaborative Filtering
Jeongwhan Choi, Seoyoung Hong, Noseong Park +1
Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factoriza…
Linear, or Non-Linear, That is the Question!
Taeyong Kong, Taeri Kim, Jinsung Jeon +4
There were fierce debates on whether the non-linear embedding propagation of GCNs is appropriate to GCN-based recommender systems. It was recently found that the linear embedding p…
LT-OCF: Learnable-Time ODE-based Collaborative Filtering
Jeongwhan Choi, Jinsung Jeon, Noseong Park
Collaborative filtering (CF) is a long-standing problem of recommender systems. Many novel methods have been proposed, ranging from classical matrix factorization to recent graph c…