1 citations · 1 across the 2 of their papers we have counts for
4 papers
FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System
Yongsen Zheng, Ziliang Chen, Jinghui Qin +1
The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow range of information or conten…
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Yongsen Zheng, Ruilin Xu, Ziliang Chen +4
The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.e.}, the rich get richer and the poor get poorer, wherein popular items are overexposed while less pop…
Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration
Tianshui Chen, Weihang Wang, Tao Pu +4
Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confiden…
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion
Jinghui Qin, Lihuang Fang, Ruitao Lu +2
Deep learning-based hyperspectral image (HSI) super-resolution, which aims to generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image (HSI) and multispectral im…