ICLR 2026 - Submissions

SubmissionsReviews

Submissions

Summary Statistics

Quantity AI Content Count Avg Rating
0-10% 1 (100%) 4.67
10-30% 0 (0%) N/A
30-50% 0 (0%) N/A
50-70% 0 (0%) N/A
70-90% 0 (0%) N/A
90-100% 0 (0%) N/A
Total 1 (100%) 4.67
Title Abstract Avg Rating Quantity AI Content Reviews Pangram Dashboard
RHGCL: Representation-Driven Hierarchical Graph Contrastive Learning for User-Item Recommendation Graph Contrastive Learning (GCL), which fuses graph neural networks with contrastive learning, has evolved as a pivotal tool in user-item recommendations. While promising, existing GCL methods often l... 4.67 0% See Reviews View AI Dashboard
PreviousPage 1 of 1 (1 total rows)Next