438 citations · 2.3k across the 30 of their papers we have counts for
8 papers · 1 filter
GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
Zhonghang Li, Lianghao Xia, Yong Xu +1
In recent years, there has been a rapid development of spatio-temporal prediction techniques in response to the increasing demands of traffic management and travel planning. While…
Graph Transformer for Recommendation
Chaoliu Li, Lianghao Xia, Xubin Ren +3
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We hi…
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Chengliang Liu, Jie Wen, Zhihao Wu +3
Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To…
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
Chengliang Liu, Jie Wen, Xiaoling Luo +3
In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the…
Graph-less Collaborative Filtering
Lianghao Xia, Chao Huang, Jiao Shi +1
Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with t…
Disentangled Graph Social Recommendation
Lianghao Xia, Yizhen Shao, Chao Huang +3
Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation r…
Heterogeneous Graph Contrastive Learning for Recommendation
Mengru Chen, Chao Huang, Lianghao Xia +3
Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve hetero…
Multi-Behavior Graph Neural Networks for Recommender System
Lianghao Xia, Chao Huang, Yong Xu +2
Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recen…