domain adaptation 1expert-guided models 1fake news detection 1knowledge distillation 1multimodal learning 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.CL2026
Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation
Xuan Feng, Guihong Liu, Tianlong Gu +5
The paper introduces Expert-Guided Mutual Distillation (EGMD), a method that improves multimodal fake news detection across domains by calibrating input coherence, aligning domain…
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
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…