3 papers
cs.LG2026
Variational Rectification Inference for Learning with Noisy Labels
Haoliang Sun, Qi Wei, Lei Feng +4
Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (\textit{e.g.}, re…
cs.IR2025
I-MRec: Invariant Learning with Information Bottleneck for Incomplete Modality Recommendation
Huilin Chen, Miaomiao Cai, Fan Liu +3
Multimodal recommender systems (MRS) improve recommendation performance by integrating complementary semantic information from multiple modalities. However, the assumption of compl…
cs.IR2025
User Invariant Preference Learning for Multi-Behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Fan Liu +2
In multi-behavior recommendation scenarios, analyzing users' diverse behaviors, such as click, purchase, and rating, enables a more comprehensive understanding of their interests,…