483 citations · 3.2k across the 44 of their papers we have counts for
13 papers · 1 filter
Quantifying the Impact of Label Noise on Federated Learning
Shuqi Ke, Chao Huang, Xin Liu
Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing studies…
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
Chunhui Zhang, Chao Huang, Yijun Tian +5
Even pruned by the state-of-the-art network compression methods, Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs,…
Self-Supervised Hypergraph Transformer for Recommender Systems
Lianghao Xia, Chao Huang, Chuxu Zhang
Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN…
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +3
Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recomme…
Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
Lianghao Xia, Chao Huang, Yong Xu +1
Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications. Hence, sequential recommender s…
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
Yijun Tian, Chuxu Zhang, Zhichun Guo +3
Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborativ…
Knowledge Graph Contrastive Learning for Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +1
Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fru…
Mutual Distillation Learning Network for Trajectory-User Linking
Wei Chen, Shuzhe Li, Chao Huang +3
Trajectory-User Linking (TUL), which links trajectories to users who generate them, has been a challenging problem due to the sparsity in check-in mobility data. Existing methods i…
Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction
Zhonghang Li, Chao Huang, Lianghao Xia +2
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for…
Hypergraph Contrastive Collaborative Filtering
Lianghao Xia, Chao Huang, Yong Xu +3
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interact…
Incentivizing Data Contribution in Cross-Silo Federated Learning
Chao Huang, Shuqi Ke, Charles Kamhoua +2
In cross-silo federated learning, clients (e.g., organizations) train a shared global model using local data. However, due to privacy concerns, the clients may not contribute enoug…
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
Wei Wei, Chao Huang, Lianghao Xia +3
A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, soci…
Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
Lianghao Xia, Chao Huang, Yong Xu +3
As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project…