1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2026
Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation
Xuan Feng, Guihong Liu, Tianlong Gu +5
Multimodal fake news detectors often generalize poorly across domains because they learn to trust unreliable evidence: domain-specific shortcuts amplified by imbalanced data and se…
cs.IR2026
Beyond Instance-Level Alignment and Uniformity: Semantic Factor Learning for Collaborative Filtering
Yajie Yu, Chenzhong Bin, Zhoubo Xu +4
Collaborative filtering (CF) is widely used in recommender systems (RecSys) due to its simplicity and efficiency. However, existing CF methods follow an instance-level learning par…
cs.IR2025★ 1 cited
Improving Recommendation Fairness via Graph Structure and Representation Augmentation
Tongxin Xu, Wenqiang Liu, Chenzhong Bin +3
Graph Convolutional Networks (GCNs) have become increasingly popular in recommendation systems. However, recent studies have shown that GCN-based models will cause sensitive inform…