438 citations · 2.3k across the 30 of their papers we have counts for
8 papers · 1 filter
Localized Sparse Incomplete Multi-view Clustering
Chengliang Liu, Zhihao Wu, Jie Wen +2
Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in re…
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…
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…
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…
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…