1 code implementation • 14 Apr 2024 • Tai Hasegawa, Sukwon Yun, Xin Liu, Yin Jun Phua, Tsuyoshi Murata
Leveraging these modified representations, DEGNN subsequently addresses downstream tasks, ensuring robustness against noise present in both edges and node features of real-world graphs.
1 code implementation • 18 Aug 2023 • Yunhak Oh, Sukwon Yun, Dongmin Hyun, Sein Kim, Chanyoung Park
Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music.
1 code implementation • 16 Aug 2023 • Junghurn Kim, Sukwon Yun, Chanyoung Park
Existing studies for applying the mixup technique on graphs mainly focus on graph classification tasks, while the research in node classification is still under-explored.
1 code implementation • 17 Apr 2023 • Kibum Kim, Dongmin Hyun, Sukwon Yun, Chanyoung Park
The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items.
1 code implementation • 22 Aug 2022 • Sukwon Yun, Kibum Kim, Kanghoon Yoon, Chanyoung Park
After having trained an expert for each balanced subset, we adopt knowledge distillation to obtain two class-wise students, i. e., Head class student and Tail class student, each of which is responsible for classifying nodes in the head classes and tail classes, respectively.