1.5k citations · 4.9k across the 104 of their papers we have counts for
9 papers · 1 filter
Independence Promoted Graph Disentangled Networks
Yanbei Liu, Xiao Wang, Shu Wu +1
We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node repres…
Learning Preferences and Demands in Visual Recommendation
Qiang Liu, Shu Wu, Liang Wang
Visual information is an important factor in recommender systems, in which users' selections consist of two components: \emph{preferences} and \emph{demands}. Some studies has been…
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Fenyu Hu, Yanqiao Zhu, Shu Wu +3
Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convoluti…
Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
Mengqi Zhang, Shu Wu, Meng Gao +3
The problem of session-aware recommendation aims to predict users' next click based on their current session and historical sessions. Existing session-aware recommendation methods…
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
Zekun Li, Zeyu Cui, Shu Wu +2
Click-through rate (CTR) prediction is an essential task in web applications such as online advertising and recommender systems, whose features are usually in multi-field form. The…
Semi-supervised Compatibility Learning Across Categories for Clothing Matching
Zekun Li, Zeyu Cui, Shu Wu +2
Learning the compatibility between fashion items across categories is a key task in fashion analysis, which can decode the secret of clothing matching. The main idea of this task i…
Learning Vertex Convolutional Networks for Graph Classification
Lu Bai, Lixin Cui, Shu Wu +2
In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification. Our idea is to transform the g…
Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
Zeyu Cui, Zekun Li, Shu Wu +2
With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of…
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Fenyu Hu, Yanqiao Zhu, Shu Wu +2
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation a…