Area of research
Artificial Intelligence · Information Systems
Research interest
Research interests include Computer science, Artificial intelligence, Data mining, Recommender system, Graph, and Machine learning.
Large Language Model Simulator for Cold-Start Recommendation
A Survey of Graph Neural Networks in Real World: Imbalance, Noise, Privacy and OOD Challenges
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation With Pre-Training
A Parameter-Efficient Federated Framework for Streaming Time Series Anomaly Detection via Lightweight Adaptation
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
A Robust and Generalized Framework for Adversarial Graph Embedding
Bayes-Enhanced Multi-View Attention Networks for Robust POI Recommendation
Multivariate Correlation-aware Spatio-temporal Graph Convolutional Networks for Multi-scale Traffic Prediction
Multi-View Tensor Graph Neural Networks Through Reinforced Aggregation
Fine-grained Urban Flow Inference with Incomplete Data
Multi-source and heterogeneous marine hydrometeorology spatio-temporal data analysis with machine learning: a survey
Group Reassignment for Dynamic Edge Partitioning
Deep Learning for Spatio-Temporal Data Mining: A Survey
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Extracting diverse-shapelets for early classification on time series
Heterogeneous Graph Embedding for Cross-Domain Recommendation Through Adversarial Learning
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
Adversarial Learning for Weakly-Supervised Social Network Alignment
Partially Shared Adversarial Learning For Semi-supervised Multi-platform User Identity Linkage
CDLFM: cross-domain recommendation for cold-start users via latent feature mapping
Understanding Information Diffusion via Heterogeneous Information Network Embeddings
Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks
Distribution Distance Minimization for Unsupervised User Identity Linkage
Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping
SSDMV: Semi-Supervised Deep Social Spammer Detection by Multi-view Data Fusion
Efficient Traffic Estimation With Multi-Sourced Data by Parallel Coupled Hidden Markov Model
Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data
Review-Based Cross-Domain Recommendation Through Joint Tensor Factorization