219 citations · 785 across the 14 of their papers we have counts for
14 papers
Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
Lianghao Xia, Chao Huang, Yong Xu +3
Many previous studies aim to augment collaborative filtering with deep neural network techniques, so as to achieve better recommendation performance. However, most existing deep le…
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Lianghao Xia, Chao Huang, Yong Xu +4
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, cri…
Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks
Huance Xu, Chao Huang, Yong Xu +3
Social recommendation which aims to leverage social connections among users to enhance the recommendation performance. With the revival of deep learning techniques, many efforts ha…
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Xiyue Zhang, Chao Huang, Yong Xu +5
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public ri…
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
Lianghao Xia, Chao Huang, Yong Xu +3
Capturing users' precise preferences is of great importance in various recommender systems (eg., e-commerce platforms), which is the basis of how to present personalized interestin…
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
Lianghao Xia, Chao Huang, Yong Xu +5
Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling users' prefe…
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
Chao Huang, Jiahui Chen, Lianghao Xia +6
Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existi…
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
Chao Huang, Huance Xu, Yong Xu +7
Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborat…
Graph Meta Network for Multi-Behavior Recommendation
Lianghao Xia, Yong Xu, Chao Huang +2
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions. In practical recommendation scenarios, use…
Social Recommendation with Self-Supervised Metagraph Informax Network
Xiaoling Long, Chao Huang, Yong Xu +4
In recent years, researchers attempt to utilize online social information to alleviate data sparsity for collaborative filtering, based on the rationale that social networks offers…
Few-Shot Knowledge Graph Completion
Chuxu Zhang, Huaxiu Yao, Chao Huang +3
Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instance…
Heterogeneous Deep Graph Infomax
Yuxiang Ren, Bo Liu, Chao Huang +3
Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used…
Neural Tensor Factorization
Xian Wu, Baoxu Shi, Yuxiao Dong +2
Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data. However, they are also limited in their ass…
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
Keith Feldman, Louis Faust, Xian Wu +2
From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation br…