H
Hongxu Chen
Researcher at University of Queensland
Publications - 36
Citations - 1073
Hongxu Chen is an academic researcher from University of Queensland. The author has contributed to research in topics: Computer science & Embedding. The author has an hindex of 12, co-authored 18 publications receiving 524 citations. Previous affiliations of Hongxu Chen include University of Technology, Sydney.
Papers
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Proceedings ArticleDOI
PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction
TL;DR: A novel heterogenous information network embedding model PME based on the metric learning to capture both first-order and second-order proximities in a unified way is proposed and the experimental results show superiority of the proposed PME model in terms of prediction accuracy and scalability.
Proceedings ArticleDOI
Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling
TL;DR: A unified model, Grid-Embedding based Multi-task Learning (GEML) which consists of two components focusing on spatial and temporal information respectively, designed to model the spatial mobility patterns of passengers and neighboring relationships of different areas is proposed.
Proceedings ArticleDOI
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction
TL;DR: A novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner is proposed that overcomes data insufficiency problem of existing work and does not necessarily rely on user demographic information.
Proceedings ArticleDOI
SPTF: A Scalable Probabilistic Tensor Factorization Model for Semantic-Aware Behavior Prediction
TL;DR: This paper proposes a scalable probabilistic tensor factorization model (SPTF) for heterogeneous behavior data and develops a novel negative sampling technique to optimize SPTF by leveraging both observed and unobserved examples with much lower computational costs and higher modeling accuracy.
Proceedings ArticleDOI
AIR: Attentional Intention-Aware Recommender Systems
TL;DR: In this paper, AIR is proposed, namely attentional intention-aware recommender systems to predict category-wise future user intention and collectively exploit the rich heterogeneous user interaction behaviors to capture varied effect of different types of actions for recommendation.