4 citations · 8 across the 10 of their papers we have counts for
7 papers · 1 filter
Large Language Model Enhanced Graph Invariant Contrastive Learning for Out-of-Distribution Recommendation
Jiahao Liang, Haoran Yang, Xiangyu Zhao +4
Out-of-distribution (OOD) generalization has emerged as a significant challenge in graph recommender systems. Traditional graph neural network algorithms often fail because they le…
Democratic Recommendation with User and Item Representatives Produced by Graph Condensation
Jiahao Liang, Haoran Yang, Xiangyu Zhao +4
The challenges associated with large-scale user-item interaction graphs have attracted increasing attention in graph-based recommendation systems, primarily due to computational in…
A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection
Kaixiang Yang, Jiarong Liu, Yupeng Song +2
Time series anomaly detection is widely used in IoT and cyber-physical systems, yet its evaluation remains challenging due to diverse application objectives and heterogeneous metri…
Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection
Zhijie Zhong, Zhiwen Yu, Kaixiang Yang +3
Time series anomaly detection (TSAD) is a critical data mining task often constrained by label scarcity. Consequently, current research predominantly focuses on Unsupervised Time-s…
ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection
Tao Yin, Xiaohong Zhang, Shaochen Fu +5
One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, traditional anomaly detectio…
CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection
Zhijie Zhong, Zhiwen Yu, Yiu-ming Cheung +1
Time Series Anomaly Detection metrics serve as crucial tools for model evaluation. However, existing metrics suffer from several limitations: insufficient discriminative power, str…
RoHyDR: Robust Hybrid Diffusion Recovery for Incomplete Multimodal Emotion Recognition
Yuehan Jin, Xiaoqing Liu, Yiyuan Yang +3
Multimodal emotion recognition analyzes emotions by combining data from multiple sources. However, real-world noise or sensor failures often cause missing or corrupted data, creati…